research essay on social media

Feb 15, 2023

6 Example Essays on Social Media | Advantages, Effects, and Outlines

Got an essay assignment about the effects of social media we got you covered check out our examples and outlines below.

Social media has become one of our society's most prominent ways of communication and information sharing in a very short time. It has changed how we communicate and has given us a platform to express our views and opinions and connect with others. It keeps us informed about the world around us. Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn have brought individuals from all over the world together, breaking down geographical borders and fostering a genuinely global community.

However, social media comes with its difficulties. With the rise of misinformation, cyberbullying, and privacy problems, it's critical to utilize these platforms properly and be aware of the risks. Students in the academic world are frequently assigned essays about the impact of social media on numerous elements of our lives, such as relationships, politics, and culture. These essays necessitate a thorough comprehension of the subject matter, critical thinking, and the ability to synthesize and convey information clearly and succinctly.

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We will provide various examples of social media essays so you may get a feel for the genre.

6 Examples of Social Media Essays

Here are 6 examples of Social Media Essays:

The Impact of Social Media on Relationships and Communication

Introduction:.

The way we share information and build relationships has evolved as a direct result of the prevalence of social media in our daily lives. The influence of social media on interpersonal connections and conversation is a hot topic. Although social media has many positive effects, such as bringing people together regardless of physical proximity and making communication quicker and more accessible, it also has a dark side that can affect interpersonal connections and dialogue.

Positive Effects:

Connecting People Across Distances

One of social media's most significant benefits is its ability to connect individuals across long distances. People can use social media platforms to interact and stay in touch with friends and family far away. People can now maintain intimate relationships with those they care about, even when physically separated.

Improved Communication Speed and Efficiency

Additionally, the proliferation of social media sites has accelerated and simplified communication. Thanks to instant messaging, users can have short, timely conversations rather than lengthy ones via email. Furthermore, social media facilitates group communication, such as with classmates or employees, by providing a unified forum for such activities.

Negative Effects:

Decreased Face-to-Face Communication

The decline in in-person interaction is one of social media's most pernicious consequences on interpersonal connections and dialogue. People's reliance on digital communication over in-person contact has increased along with the popularity of social media. Face-to-face interaction has suffered as a result, which has adverse effects on interpersonal relationships and the development of social skills.

Decreased Emotional Intimacy

Another adverse effect of social media on relationships and communication is decreased emotional intimacy. Digital communication lacks the nonverbal cues and facial expressions critical in building emotional connections with others. This can make it more difficult for people to develop close and meaningful relationships, leading to increased loneliness and isolation.

Increased Conflict and Miscommunication

Finally, social media can also lead to increased conflict and miscommunication. The anonymity and distance provided by digital communication can lead to misunderstandings and hurtful comments that might not have been made face-to-face. Additionally, social media can provide a platform for cyberbullying , which can have severe consequences for the victim's mental health and well-being.

Conclusion:

In conclusion, the impact of social media on relationships and communication is a complex issue with both positive and negative effects. While social media platforms offer many benefits, such as connecting people across distances and enabling faster and more accessible communication, they also have a dark side that can negatively affect relationships and communication. It is up to individuals to use social media responsibly and to prioritize in-person communication in their relationships and interactions with others.

The Role of Social Media in the Spread of Misinformation and Fake News

Social media has revolutionized the way information is shared and disseminated. However, the ease and speed at which data can be spread on social media also make it a powerful tool for spreading misinformation and fake news. Misinformation and fake news can seriously affect public opinion, influence political decisions, and even cause harm to individuals and communities.

The Pervasiveness of Misinformation and Fake News on Social Media

Misinformation and fake news are prevalent on social media platforms, where they can spread quickly and reach a large audience. This is partly due to the way social media algorithms work, which prioritizes content likely to generate engagement, such as sensational or controversial stories. As a result, false information can spread rapidly and be widely shared before it is fact-checked or debunked.

The Influence of Social Media on Public Opinion

Social media can significantly impact public opinion, as people are likelier to believe the information they see shared by their friends and followers. This can lead to a self-reinforcing cycle, where misinformation and fake news are spread and reinforced, even in the face of evidence to the contrary.

The Challenge of Correcting Misinformation and Fake News

Correcting misinformation and fake news on social media can be a challenging task. This is partly due to the speed at which false information can spread and the difficulty of reaching the same audience exposed to the wrong information in the first place. Additionally, some individuals may be resistant to accepting correction, primarily if the incorrect information supports their beliefs or biases.

In conclusion, the function of social media in disseminating misinformation and fake news is complex and urgent. While social media has revolutionized the sharing of information, it has also made it simpler for false information to propagate and be widely believed. Individuals must be accountable for the information they share and consume, and social media firms must take measures to prevent the spread of disinformation and fake news on their platforms.

The Effects of Social Media on Mental Health and Well-Being

Social media has become an integral part of modern life, with billions of people around the world using platforms like Facebook, Instagram, and Twitter to stay connected with others and access information. However, while social media has many benefits, it can also negatively affect mental health and well-being.

Comparison and Low Self-Esteem

One of the key ways that social media can affect mental health is by promoting feelings of comparison and low self-esteem. People often present a curated version of their lives on social media, highlighting their successes and hiding their struggles. This can lead others to compare themselves unfavorably, leading to feelings of inadequacy and low self-esteem.

Cyberbullying and Online Harassment

Another way that social media can negatively impact mental health is through cyberbullying and online harassment. Social media provides a platform for anonymous individuals to harass and abuse others, leading to feelings of anxiety, fear, and depression.

Social Isolation

Despite its name, social media can also contribute to feelings of isolation. At the same time, people may have many online friends but need more meaningful in-person connections and support. This can lead to feelings of loneliness and depression.

Addiction and Overuse

Finally, social media can be addictive, leading to overuse and negatively impacting mental health and well-being. People may spend hours each day scrolling through their feeds, neglecting other important areas of their lives, such as work, family, and self-care.

In sum, social media has positive and negative consequences on one's psychological and emotional well-being. Realizing this, and taking measures like reducing one's social media use, reaching out to loved ones for help, and prioritizing one's well-being, are crucial. In addition, it's vital that social media giants take ownership of their platforms and actively encourage excellent mental health and well-being.

The Use of Social Media in Political Activism and Social Movements

Social media has recently become increasingly crucial in political action and social movements. Platforms such as Twitter, Facebook, and Instagram have given people new ways to express themselves, organize protests, and raise awareness about social and political issues.

Raising Awareness and Mobilizing Action

One of the most important uses of social media in political activity and social movements has been to raise awareness about important issues and mobilize action. Hashtags such as #MeToo and #BlackLivesMatter, for example, have brought attention to sexual harassment and racial injustice, respectively. Similarly, social media has been used to organize protests and other political actions, allowing people to band together and express themselves on a bigger scale.

Connecting with like-minded individuals

A second method in that social media has been utilized in political activity and social movements is to unite like-minded individuals. Through social media, individuals can join online groups, share knowledge and resources, and work with others to accomplish shared objectives. This has been especially significant for geographically scattered individuals or those without access to traditional means of political organizing.

Challenges and Limitations

As a vehicle for political action and social movements, social media has faced many obstacles and restrictions despite its many advantages. For instance, the propagation of misinformation and fake news on social media can impede attempts to disseminate accurate and reliable information. In addition, social media corporations have been condemned for censorship and insufficient protection of user rights.

In conclusion, social media has emerged as a potent instrument for political activism and social movements, giving voice to previously unheard communities and galvanizing support for change. Social media presents many opportunities for communication and collaboration. Still, users and institutions must be conscious of the risks and limitations of these tools to promote their responsible and productive usage.

The Potential Privacy Concerns Raised by Social Media Use and Data Collection Practices

With billions of users each day on sites like Facebook, Twitter, and Instagram, social media has ingrained itself into every aspect of our lives. While these platforms offer a straightforward method to communicate with others and exchange information, they also raise significant concerns over data collecting and privacy. This article will examine the possible privacy issues posed by social media use and data-gathering techniques.

Data Collection and Sharing

The gathering and sharing of personal data are significant privacy issues brought up by social media use. Social networking sites gather user data, including details about their relationships, hobbies, and routines. This information is made available to third-party businesses for various uses, such as marketing and advertising. This can lead to serious concerns about who has access to and uses our personal information.

Lack of Control Over Personal Information

The absence of user control over personal information is a significant privacy issue brought up by social media usage. Social media makes it challenging to limit who has access to and how data is utilized once it has been posted. Sensitive information may end up being extensively disseminated and may be used maliciously as a result.

Personalized Marketing

Social media companies utilize the information they gather about users to target them with adverts relevant to their interests and usage patterns. Although this could be useful, it might also cause consumers to worry about their privacy since they might feel that their personal information is being used without their permission. Furthermore, there are issues with the integrity of the data being used to target users and the possibility of prejudice based on individual traits.

Government Surveillance

Using social media might spark worries about government surveillance. There are significant concerns regarding privacy and free expression when governments in some nations utilize social media platforms to follow and monitor residents.

In conclusion, social media use raises significant concerns regarding data collecting and privacy. While these platforms make it easy to interact with people and exchange information, they also gather a lot of personal information, which raises questions about who may access it and how it will be used. Users should be aware of these privacy issues and take precautions to safeguard their personal information, such as exercising caution when choosing what details to disclose on social media and keeping their information sharing with other firms to a minimum.

The Ethical and Privacy Concerns Surrounding Social Media Use And Data Collection

Our use of social media to communicate with loved ones, acquire information, and even conduct business has become a crucial part of our everyday lives. The extensive use of social media does, however, raise some ethical and privacy issues that must be resolved. The influence of social media use and data collecting on user rights, the accountability of social media businesses, and the need for improved regulation are all topics that will be covered in this article.

Effect on Individual Privacy:

Social networking sites gather tons of personal data from their users, including delicate information like search history, location data, and even health data. Each user's detailed profile may be created with this data and sold to advertising or used for other reasons. Concerns regarding the privacy of personal information might arise because social media businesses can use this data to target users with customized adverts.

Additionally, individuals might need to know how much their personal information is being gathered and exploited. Data breaches or the unauthorized sharing of personal information with other parties may result in instances where sensitive information is exposed. Users should be aware of the privacy rules of social media firms and take precautions to secure their data.

Responsibility of Social Media Companies:

Social media firms should ensure that they responsibly and ethically gather and use user information. This entails establishing strong security measures to safeguard sensitive information and ensuring users are informed of what information is being collected and how it is used.

Many social media businesses, nevertheless, have come under fire for not upholding these obligations. For instance, the Cambridge Analytica incident highlighted how Facebook users' personal information was exploited for political objectives without their knowledge. This demonstrates the necessity of social media corporations being held responsible for their deeds and ensuring that they are safeguarding the security and privacy of their users.

Better Regulation Is Needed

There is a need for tighter regulation in this field, given the effect, social media has on individual privacy as well as the obligations of social media firms. The creation of laws and regulations that ensure social media companies are gathering and using user information ethically and responsibly, as well as making sure users are aware of their rights and have the ability to control the information that is being collected about them, are all part of this.

Additionally, legislation should ensure that social media businesses are held responsible for their behavior, for example, by levying fines for data breaches or the unauthorized use of personal data. This will provide social media businesses with a significant incentive to prioritize their users' privacy and security and ensure they are upholding their obligations.

In conclusion, social media has fundamentally changed how we engage and communicate with one another, but this increased convenience also raises several ethical and privacy issues. Essential concerns that need to be addressed include the effect of social media on individual privacy, the accountability of social media businesses, and the requirement for greater regulation to safeguard user rights. We can make everyone's online experience safer and more secure by looking more closely at these issues.

In conclusion, social media is a complex and multifaceted topic that has recently captured the world's attention. With its ever-growing influence on our lives, it's no surprise that it has become a popular subject for students to explore in their writing. Whether you are writing an argumentative essay on the impact of social media on privacy, a persuasive essay on the role of social media in politics, or a descriptive essay on the changes social media has brought to the way we communicate, there are countless angles to approach this subject.

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Advances in Social Media Research: Past, Present and Future

• Open access
• Published: 06 November 2017
• Volume 20 , pages 531–558, ( 2018 )

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• Kawaljeet Kaur Kapoor 1 ,
• Kuttimani Tamilmani 2 ,
• Nripendra P. Rana 2 ,
• Pushp Patil 2 ,
• Yogesh K. Dwivedi 2 &
• Sridhar Nerur 3  

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Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field.

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1 Introduction

Social media allows relationship forming between users from distinct backgrounds, resulting in a tenacious social structure. A prominent output of this structure is the generation of massive amounts of information, offering users exceptional service value proposition. However, a drawback of such information overload is sometimes evident in users’ inability to find credible information of use to them at the time of need. Social media sites are already so deeply embedded in our daily lives that people rely on them for every need, ranging from daily news and updates on critical events to entertainment, connecting with family and friends, reviews and recommendations on products/services and places, fulfilment of emotional needs, workplace management, and keeping up with the latest in hashion, to name but a few.

When we refer to social media, applications such as Facebook, WhatsApp, Twitter, YouTube, LinkedIn, Pinterest, and Instagram often come to mind. These applications are driven by user-generated content, and are highly influential in a myriad of settings, from purchasing/selling behaviours, entrepreneurship, political issues, to venture capitalism (Greenwood and Gopal 2015 ). As of April 2017, Facebook enjoys the exalted position of being the market leader of the social media world, with 1.97 billion monthly users (Statista 2017 ). In addition to posts, social media sites are bombarded with photo and video uploads, and according to the recent numbers, about 400 million snaps a day have been recorded on Snapchat, with around 9000 photos being shared every second (Lister 2017 ). While 50 million businesses are active on Facebook business pages, two million businesses are using Facebook advertising. Apparently, 88% businesses use Twitter for marketing purposes (Lister 2017 ).

Academics and practitioners have explored and examined the many sides of social media over the past years. Organizations engage in social media mostly with the aim of obtaining feedback from stakeholders (Phang et al. 2015 ). Consumer reviews are another big part of social media, bringing issues of information quality, credibility, and authenticity to the forefront. To a large extent, online communities have been successful in bringing together people with similar interests and goals, making the concept of micro blogging very popular. While most messages exchanged on social media sites are personal statuses or updates on current affairs, some posts are support seeking, where people are looking for assistance and help. Interestingly, these have been recognized as socially exhausting posts that engender social overload, causing other members to experience negative behavioural and psychological consequences, because they feel compelled to respond (Maier et al. 2015a ).

Given the relevance of social media to various stakeholders, and the numerous consequences associated with its use, social media has attracted the attention of researchers from various fields, including information systems. This is evidenced by the large number of scholarly articles that have appeared in various outlets. Researchers have to expend an enormous amount of time and effort in collating, analysing, and synthesising findings from existing works before they embark on a new research project. Given the significant number of studies that have already been published, a comprehensive and systematic review can offer valuable assistance to researchers intending to engage in social medi research. Our literature search suggests that there are reviews on social media in the marketing context (see for example, AlAlwan et al. 2017 ; Dwivedi et al. 2017a ; Dwivedi et al. 2015 ; Ismagilova et al. 2017 ; Kapoor et al. 2016 ; Plume et al. 2016 ). However, there exists no comprehensive review that integrates and synthesises the findings from the articles published in Information Systems journals. Such an endeavour will not only provide a holistic view of the extant research on social media, but will also provide researchers a comprehensive intellectual platform that can be used to pursue fruitful lines of enquiry to help advance research in this rapidly expanding area. To fulfill this goal, this study reviewed relevant articles to elucidate the key thematic areas of research on social media, including its benefits and spill-over effects. The resulting review is expected to serve as a one-stop source, offering insight into what has been accomplished so far in terms of research on social media, what is currently being done, and what challenges and opportunities lie ahead. By doing so, this study explores the following aspects of existing research on social media:

How is social media defined in the IS literature?

How has social media literature evolved from a multidisciplinary perspective?

How have social media technologies, applications, practices, and research evolved over the past 20 years?

Which social media issues and themes have already been examined in IS research?

What are the major limitations of extant literature on social media?

The next section of this paper gives a brief overview of the method employed for carrying out the literature search. The succeeding section discusses citation and text analyses of social media publications. Subsequently, we outline the various ways in which scholars have defined social media. This is followed by a section that focuses on the evolution of social media research from an IS perspective. Next, we articulate the major themes emerging from prior research and use them as a backdrop for our review of the literature on social media. The ensuing section discusses our findings, followed by key conclusions and limitations of the study.

2 Literature Search Method

The literature search for this analysis was conducted in the following two phases: (1) keyword-based search and analysis to explore the overall evolution of social media literature; and (2) manual search across specific IS journals to understand the emerging IS perspectives on this topic.

2.1 Keywords Based Search and Analysis

In order to gain a deeper understanding of social media, we analyzed relevant abstracts that were downloaded from the Web of Science (WOS) database. Our search terms Footnote 1 yielded a total of 13,177 records, out of which 12,597 unique abstracts were obtained. The analysis of these records was undertaken in two steps. First, we used VOSviewer (Van Eck and Waltman 2011 ) to perform a co-citation analysis of first authors in the downloaded corpus. VOSviewer allows visualization of similarities in publications and authors through an examination of bibliometric networks. Furthermore, we used VOSviewer to analyze words derived from titles and abstracts. Second, we used Latent Dirichlet Allocation (LDA) (see Blei 2012 ) to extract key thematic areas latent in the literature on social media. Further details about these analyses and results are presented in section 3 .

2.2 Manual Search and Analysis

Given the inconsistencies in the use of keywords in social media research, a manual search, rather than a keyword-based one, was deemed to be more appropriate for identifying the existing literature on social media. Furthermore, since keywords in the social media literature tend to overlap with topics and/or theories in other related research areas, a keyword search may yield irrelevant articles. For instance, a keyword search for “Social network” returns articles related to social network theories, which are not necessarily part of social media. The articles reviewed in this study are from the following eight Senior Scholars’ Basket of Information Systems journals: European Journal of Information Systems (EJIS); Information Systems Journal (ISJ); Information Systems Research (ISR); Journal of the Association for Information Systems (JAIS); Journal of Information Technology (JIT); Journal of Management Information Systems (JMIS); Journal of Strategic Information Systems (JSIS) and Management Information Systems Quarterly (MISQ)). Along with these eight journals, we have also analysed relevant articles from Information Systems Frontier (ISF) journal. This is because it focuses on examining “new research and development at the interface of information systems (IS) and information technology (IT) from analytical, behavioural, and technological perspectives. It provides a common forum for both frontline industrial developments as well as pioneering academic research”. Footnote 2 ISF enjoys the reputation of a high quality journal across continents. For example, a journal quality ranking by Chartered Association of Business Schools, UK, has given it a three star (high ranking) quality rating, while journal ranking by the Australian Business Deans Council (ABDC) has rated it as an ‘A’ class journal (the second highest quality journal category after A*, which is reserved for premier publications). In light of these observations, it was deemed appropriate to consider articles from ISF along with the aforementioned eight journals.

Relevant articles were then identified and downloaded from each of the target journals by going through their archives. Specifically, all volumes and issues published in these journals between 1997 and 2017 were considered in our analysis. Articles, research notes, introductions, research commentaries, and editorial overviews relevant to social media were downloaded and numbered to prepare an APA style reference list. The first literature search resulted in 181 articles that had some relevance to the social media domain. A closer examination of individual abstracts and full articles led to the elimination of 49 irrelevant articles, thus giving us a total of 132 articles pertinent to the domain of interest (i.e., social media).

3 Citation and Text Analyses of Social Media Publications

3.1 author co-citation analysis (aca).

Author Co-Citation Analysis (ACA) is a bibliometric technique that has been widely used to explicate the conceptual structure of disciplines (for example, see White and Griffith 1981 ; McCain 1984 ; Culnan 1986 ; Nerur et al. 2008 ). The underlying assumption in ACA is that authors who are frequently cited together tend to work on similar concepts. Thus, frequently co-cited authors are likely to cluster together when an ACA is performed. VOSviewer considers only first authors when it performs ACA. Only authors who had 50 or more citations were included in the analysis. Figure  1 shows the results of ACA.

Author clusters from ACA

VOSviewer identified seven distinct clusters:

Cluster 1: Authors in this cluster have contributed to research on Twitter (e.g., Sakaki), social network analysis (e.g., Wasserman), topic modeling (e.g., Blei), sociality and cognition (e.g., Dunbar), sentiment analysis of tweets (e.g., Thelwall), and other related topics.

Cluster 2: Authors in this cluster are well known for their work on technology adoption (e.g., Venkatesh), diffusion of technology (Rogers), culture (Hofstede), theory of planned behavior (Ajzen), marketing/consumer behavior (e.g., Hennig-Thurau), and statistical methods (e.g., Bagozzi, Fornell, Hair).

Cluster 3: This cluster comprises of authors who deal with a variety of issues related to social media (Facebook and Twitter) use. For example, Steinfied and Ellison examined social capital across Facebook; Kuss studied online/social networking addiction (e.g., gaming addiction), and Lenhart focused on teens and technology (e.g., mobile internet use), particularly in the use of social media. Other topics include Bandura’s self-efficacy, use and benefits of Twitter by scholars, and personality and social characteristics of Facebook users (e.g., Ross).

Cluster 4: Prominent social theorists/sociologists who have contributed to social capital theory, structuration theory and modern sociological theory are distinguished members of this cluster. These include Bourdieu, Coleman, Giddens, and Habermas. Papacharissi has written about a variety of topics including the exploration of factors that predict Internet use as well as users’ behaviors, identity, sense of community and culture on social media. Tufekci has studied privacy and disclosure on social media, as well as other topics, including how social networking sites such as Facebook might influence one’s decision to participate in protests.

Cluster 5: In this cluster, there is evidence of the influence of Vygotsky’s socio cultural learning theory as well as Lave and Wenger’s work on communities of practice. In addition to his work on collaborative learning, Kirschner has examined the relationship between Facebook and academic performance. Likewise, Selwyn has explored pedagogical and learning engendered by the use of information and computer technologies (ICT).

Cluster 6: This cluster appears to reflect two broad themes. The first is a range of topics related to medical Internet research, broadly referred to as e-health (Eysenbach) or online health (Duggan). Themes in this category include electronic support groups and health in virtual communities (Eysenbach), and policies and healthcare associated with social media, and professionals among medical students and physicians in the use of social media (Chretien, Greysen). The second main thematic area in this cluster deals with scholarship on social media, scholarly communication, and metrics for evaluating impact of articles on the web (e.g., Weller, Bormann, Priem).

Cluster 7: The dominant theme here is the nature and content of communication. In particular, scholars in this cluster have focused on communication and response in the face of crises (Coombs), including image restoration after a controversy (Benoit), analysis and reliability of content (Krippendorff), and the use of social media sites such as Facebook and Twitter by government agencies and non-profit organizations to engage stakeholders (Waters).

3.2 Text Analysis of Words in Titles and Abstracts

VOSviewer was used to analyze terms (i.e., words) in the titles and abstracts of our corpus to obtain a two-dimensional map showing proximities of words that are likely to be related based on their co-occurrences. Specifically, VOSviewer relies on the Apache OpenNLP Toolkit to identify noun phrases, and then compares their overall co-occurrence distribution with their distribution across other noun phrases to compute a relevance score (Van Eck and Waltman 2011 ). The intuition is that frequently co-occurring noun phrases with high relevance are likely to unravel a topic or theme that is latent in the corpus. The term map from VOSviewer is shown in Fig.  2 . Only terms that occurred 50 times or more were included. Furthermore, relevance scores computed by VOSviewer for every term were used to select the top 80% that met the threshold.

Term map showing clusters of related words/noun phrases

VOSviewer identified five clusters here. It is evident from the clusters that research on social media has dealt with a broad range of topics, including but not restricted to diffusion of information and opinions, spread of diseases (e.g., influenza), identification of social and emotional health concerns and attendant interventions to deal with them, social media as an influence, the use of social media for marketing purposes, and the implications of social media as a tool for pedagogy (i.e., teaching and learning) and medical practice. These have been summarized in Table  1 .

It must be noted that the topics are broad and don’t reveal the nuances of research areas embodied in the abstracts examined in this study. The next sub-section presents the results of topic modeling, which has the potential to unravel more focused themes embodied in the large corpus that we analyzed.

3.3 Topic Modeling

The fact that our search terms yielded over 12,000 abstracts suggests that scholars are investing increased interest on research issues related to social media. While an informed researcher may have a general idea of the nature of research undertaken so far, it is humanly impossible to discern the thematic structure of all scholarly documents available on social media. Recent advances in topic modeling have made this task relatively easy. Topic modeling relies on algorithms and statistical methods to elicit the topics latent in a large corpus (Blei 2012 ). The term topic refers to a specific and often recognizable theme defined by a cohesive set of words that have a high probability of belonging to that topic. There are several options available for topic modeling: non-negative matrix factorization (NNMF), Latent Semantic Analysis/Indexing (LSA/LSI), and Latent Dirichlet Allocation (LDA). In this study, we use LDA, arguably the most widely used topic modeling algorithm. In order to perform topic modeling on a corpus, the researcher has to specify the number of topics to be extracted. In this study, we extracted the top 100 topics reflected in the scholarship on social media. LDA starts with the assumption that each abstract in our study reflects each of these topics to varying degrees (Blei 2012 ). Thus, each abstract has a distribution of the desired 100 topics. The 100 topics that were extracted from our abstracts are shown in Table  2 . The machine learning for language toolkit (MALLET) (McCallum 2002 ) was used for this purpose.

4 Analysis of Social Media Research from an IS Perspective

4.1 how is social media defined in the is literature.

In studying the existing literature on social media, it becomes apparent that the authors in this field have not focussed on defining social media. Of all the studies included in this review, only a handful of studies have come close to defining, or clarifying the concept of social media. For instance, Lundmark et al. ( 2016 , p3) suggest, “social media, as a unique form of communication, integrates multiple sources of legitimacy, and as a result, presents a unique and important context through which to study the topic. Indeed, social media are a means for the dissemination of both internally and externally generated information pertaining to firms, industries, and society in general.” According to Schlagwein and Hu ( 2016 ), social media constitutes internet-based communication and collaboration channels, widely in use since 2005, and, from an IS perspective, social media tools and their surrounding organizational and managerial structures constitute social information systems. Wakefield and Wakefield ( 2016 , p140) describe “social media technologies as an ensemble IS artefact composed of technical, informational, and relational subsystems that interact distinctly according to the context of use.” In their study, they also identify a “recent definition of social media and social networks referring to social media networks as specific types of social media platforms and Internet sites with common attributes such as (1) user profile (2) user access to digital content (3) a user list of relational ties, and (4) user ability to view and traverse relational ties” (Wakefield and Wakefield 2016 ; p144).

In a more relatable and simple definition, Miranda et al. ( 2016 ; p304) explain social media being “mainly conceived of as a medium wherein ordinary people in ordinary social networks (as opposed to professional journalists) can create user-generated news.” A few other authors like Spagnoletti et al. ( 2015 ) and Xu and Zhang ( 2013 ) commonly refer to social media as a set of interned-based technologies/applications, which are aimed at promoting the creation, modification, update and exchange of user-generated content, whilst establishing new links between the content creators themselves. Bharati et al. ( 2014 ; p258) refer to social media as a technology “not focussed on transactions but on collaboration and communication across groups both inside and outside the firm.” Lastly, Tang et al. ( 2012 ; p44) also identify social media as user-generated media, which is a source of “online information created, initiated, circulated, and used by consumers intent on educating each other about products, brands, services, personalities, and issues.”

All of the aforementioned descriptions clearly regard social media as communication tools supported by internet-based technologies for dissemination of information. Most of them acknowledge the high concentration of user generated content across such platforms. Based on our understanding of social media and the aforementioned definitions, we propose the following definition: Social media is made up of various user-driven platforms that facilitate diffusion of compelling content, dialogue creation, and communication to a broader audience. It is essentially a digital space created by the people and for the people, and provides an environment that is conducive for interactions and networking to occur at different levels (for instance, personal, professional, business, marketing, political,and societal) .

4.2 Evolution of Social Media Research in the IS Literature

In the past two decades, various issues related to social media have been examined in line with the rapid evolution of underlying technologies/applications and their appropriation to enable different types of social media usage. An analysis of 132 articles from selected IS journals suggests that publications until 2011 were still examining user-generated content as a new type of online content (Burgess et al. 2011 ). However, in the last six years, research in this field has made tremendous progress, not just in terms of its scope, but also in explicating the highs and lows associated with the use of social media. While it is difficult to pinpoint evolution on a yearly basis, it has been possible to identify the major aspects of social media research that have emerged over time. Publications between 1997 and 2017 have been reviewed here. Interestingly, only one publication of interest to this study (Griffiths and Light 2008 ) was identified between the period 1997 and 2009.

Out of the 132 studies individually reviewed here, about 21 studies examined the behavioural side of social media use. While most of the initial studies (for instance, Massari 2010 ; Garg et al. 2011 ) restricted interest to peer influence and information disclosure willingness (2010–2012), the latter studies (for instance, Gu et al. 2014 ; Krasnova et al. 2015 ) were seen to be more exploratory in examining the positive, dysfunctional, cognitive and affective, heterophily and homophily tendencies of social media users (2012–2016). There were 18 studies investigating the very popular aspect of reviews and recommendations on social networks, with 2013 being a popular year for such studies. Most of these studies (for instance, Hildebrand et al. 2013 ; Zhang and Piramuthu 2016 ) were interested in improving their understanding of the information quality of these reviews and the associated consequences (2010–2016). There were 17 studies (2011–2016) evaluating the integration of social media for varied organizational purposes . While some studies investigated the employee side (e.g., innovativeness, retention, and motivation) of social media use (for instance, Aggarwal et al. 2012 ; Miller and Tucker 2013 ), the others discussed the relationship between social enterprise systems and organizational networking (for instance, Trier and Richter 2015 ; Van Osch and Steinfield 2016 ).

Around 13 publications studied the use of social media as a marketing tool . The early studies here (2010–2013) explored consumer purchase behaviour and firm tactics, such as involving consumers in marketing strategies (for instance, García-Crespo et al. 2010 ; Goh et al. 2013 ). The later studies (2015–2016), however, became more focussed on studying social commerce across networking sites such as Facebook, MySpace, and YouTube (e.g., Chen et al. 2015 ; Sung et al. 2016 ). Ten studies were interested in online communities and blogging (see Singh et al. 2014 ; Dennis et al. 2016 ). These were mostly interested in blogger behaviours, reader retention, online content, contributing capacity, and blog visibility (2011–2016). Nine publications revealed the risks associated with the use of social media. These are either very early studies (2008–2010; for instance, Tow et al. 2010 ) or fairly recent (2014–2016) learning about scamming and farcing issues faced by users. They focus on combating issues of privacy and security, whilst trying to differentiate between fake and authentic online content (for instance, Zhang et al. 2016 ).

Up until 2015, about eight studies analysed the negative stigma attached to using social media at the workplace (for instance, Koch et al. 2013 ). While a couple of studies also revealed the positive side of social media (for instance, Lu et al. 2015 ), most were seen discussing its ill-effects on work outputs, routine performance, and clash of notions in the personal and professional space (for instance, Ali-Hassan et al. 2015 ). About seven studies were interested in exploring the relationship between social media use and value creation (for instance, Luo et al. 2013 ; Barrett et al. 2016 ) in terms of firm equity, customer retention, social position, and firm value (2010–2016). Another seven studies investigated the use of media sites to share and exchange information during natural disasters and critical events (2011–2015). Interestingly, most of the studies documenting this aspect of social media used Twitter data for their analyses (for instance, Oh et al. 2013 ; Lee et al. 2015a ). A very small percentage of studies (five studies) in 2014 and 2015 focussed on analysing the effects of social media posts that were seeking help/support from other social media users (for instance, Spagnoletti et al. 2015 ; Yan et al. 2015a ). Only a handful of studies (five studies), particularly in 2010 and 2016, were examined the use of social media in public administration and political contexts, such as open governance and transparency (for instance, Baur 2017 ; Rosenberger et al. 2017 ). Also, just about three studies (Wattal et al. 2010 ; Dewan and Ramaprasad 2014 ; Miranda et al. 2016 ) dedicated their efforts to comparing traditional media with social media . The last set of studies (2013–2016), around nine in total (for instance, Bharati et al. 2014 ; Chung et al. 2017 ), were identified as those limiting themselves to developing and testing social media constructs in relation to previously established theories and models (technology acceptance model, theory of planned behaviour, and others).

4.3 Literature Synthesis

As outlined in the previous section, social media research is evolving at a fast pace. In reviewing the shortlisted articles, various themes were identified based on the similarities observed across the issues addressed in social media research.

4.3.1 Social Media Use Behaviours and Consequences

Many scholars explore the behavioural side of social media, and interestingly, some find factors that prevent users from continuing its use. Turel and Serenko ( 2012 ) warn against excessive use of social media sites, which can result in strong pathological and maladaptive psychological dependency on social media. In a subsequent study, Turel ( 2015 ) used cognitive theory to reveal that guilt feelings associated with the use of a website can increase discontinuance intentions. Matook et al. ( 2015b ) show that online social networks can be linked with perceived loneliness, which depends on user’s active/passive engagement with social media. Krasnova et al. ( 2015 ) suggest that in response to social information consumption, envy plays a significant role in reducing cognitive and affective wellbeing of a user. However, Maier et al. ( 2015b ) disclose that, while social networking stress creators can increase discontinuance intentions, switching stress-creators and exhaustion (i.e. switching to alternatives) can reduce such intentions. Chang et al. ( 2014 ) find that dissatisfaction and regret, alternative attractiveness, and switching costs affect switching intentions. Xu et al. ( 2014 ) find that dissatisfaction from support and entertainment values, continuity cost and peer influence encourage switching between social networks.

Wakefield and Wakefield ( 2016 ) focus on Facebook and Twitter to show that excitement combined with passion acts as a favourable factor for increased social media engagement. Chiu and Huang ( 2015 ) use media communication theories to show that user gratification from social networking sites positively affects their social media usage intention. In studying virtual investment communities, Gu et al. ( 2014 ) reveal that despite benefits of heterophily, investors are allured by homophily in their interactions. Zeng and Wei ( 2013 ) analyse Flickr data and find that at the time of forming a social tie, members exhibit similar behaviour, which evolves differently later. Shi et al. ( 2014 ) examine retweet relationships and find that those with weak ties have a higher probability of engaging in content sharing. Kreps ( 2010 ) introduces poststructuralist critique to explore how closely an individual’s personality is reflected in their social media profile, such as Facebook.

Chen et al. ( 2014 ) find affective and continuance types of commitments to be good predictors of user behaviours on social media sites. Stieglitz and Dang-Xuan ( 2013 ) examine the relationship between user behaviour and sentiment to conclude that emotional Twitter messages have a higher retweet tendency. Khan and Jarvenpaa ( 2010 ) analyse event creation pages on Facebook to find that the social groups demonstrate differential interactive behaviour prior and post the midpoint of event creation. Chen and Sharma ( 2015 ) disclose that the extent of self-disclosure on social media sites depends on member attitude. Massari ( 2010 ) finds that MySpace users tend to disclose substantial personal details that put them at the risk of security and privacy breach. Xu et al. ( 2016 ) find that one’s image and moral beliefs combined with community policies and peer pressure act as deterrents to aggression on social media. Garg et al. ( 2011 ) measure peer influence in an online music community and find that peers can significantly increase music discovery. Susarla et al. ( 2012 ) examine video and user information dataset from YouTube, and find that the success of a video hugely depends on social interactions, which also determines its impact magnitude.

The review of studies related to this theme suggests that since 2010, IS researchers have focussed on examining the dysfunctional consequences of social media adoption, such as - addiction, stress, information overload, and others. Use behaviour was examined across a variety of platforms like Facebook, Twitter, MySpace, and Flickr. Media content, such as picture, video, and tweets have also been explored by the studies in this category.

4.3.2 Reviews and Recommendations on Social Media Sites

A predominant characteristic of social media networks is product/service reviews and recommendations. People are beginning to rely on others’ experiences, for instance, before making a purchase, visiting a place, or searching for accommodation.. Such online reviews complement product/service information. An early study on online travel information found that consumers invest higher trust in reviews published on government/tourism websites in comparison to those on a social media site (Burgess et al. 2011 ). Hwang et al. ( 2011 ) analysed the social bookmarking sites for impact of positive and negative reviews on collective wisdom and found that negative reviews are capable of stabilizing system performance. Dellarocas et al. ( 2010 ) suggest that online forums looking to increase reviews of lesser-known products should make information on previously posted reviews a less prominent feature. Cheung et al. ( 2012 ) empirically tested a consumer review website to conclude that argument quality, review consistency, and source are critical for assessing review credibility.

Chen et al. ( 2011 ) investigate the effect of moderation and reveal that the commentators generate high quality content to build a stronger reputation. Wei et al. ( 2013 ) developed a multi-collaborative filtering trust network algorithm for Web 2.0 with improved accuracy for filtering information based on user preferences and trusted peer users. Luo and Zhang ( 2013 ) refer to user-generated reviews and recommendations as consumer buzz to find that advocacy and consumer attitude can impact firm value. Hildebrand et al. ( 2013 ) use data from a European car manufacturer allowing self-designed products to reveal that feedback from other community members lessens uniqueness whilst increasing dissatisfaction. Centeno et al. ( 2015 ) address the skewed reputation rankings problem in movie ratings by suggesting the use of comparative user opinions. Ma et al. ( 2013 ) analyse data from Yelp to test bias in online reviews and find that frequent and longer reviews successfully combat such biases. Lukyanenko et al. ( 2014 ) demonstrate that participants tend to provide accurate information in classifying a phenomenon at a general level, and higher accuracy where they are allowed free form data. Shi and Whinston ( 2013 ) explore the possible impact of friend check-ins on social media, and find it has no positive effect in generating new user visits.

Goes et al. ( 2014 ) disclose that user popularity results in increased and objective reviews, while numeric ratings turn more varied and negative with it. Matook et al. ( 2015a ) use relationship theories to show that past recommendation experience, closeness, and excessive posting behaviour positively affect trust and person’s intention to act on the made recommendation. Yan et al. ( 2015b ) evaluate revisit intentions for restaurants, and find that food and service quality, price and value, and the atmosphere govern such intentions. Kuan et al. ( 2015 ) analysed Amazon reviews and observed that certain characteristics such as length, readability, valence, extremity, and reviewer credibility are more likely to be recognized. In a different study, Zhang and Piramuthu ( 2016 ) suggest that product/service information on seller’s websites are often limited, and propose a Latent Dirichlet Allocation model to reveal the useful complementary hidden information in customer reviews. In a parallel conversation, Wu and Gaytán 2013 suggest that buyers integrate product price with seller reviews in configuring their willingness to pay.

The review under this theme suggests that studies as early as 2010 focussed on evaluating the authenticity of product and service reviews/recommendations published online. Overall, these studies reveal that the effect of review volume is often moderated by a buyer’s risk attitude. Most studies identify that the combination of consumer’s interest and available reviews helps users choose products/services that offer best value to them.

4.3.3 Social Media and Associated Organizational Impact

Publications have also shown interest in investigating the effects of user-generated content on entrepreneurial behaviour. For instance, Greenwood and Gopal ( 2015 ) find that discourse in both traditional and user-generated media has a notable influence on IT firm founding rates. Lundmark et al. ( 2016 ) reveal that higher usage of Twitter, alongside follower numbers and retweets result in higher levels of under pricing for initial public offerings (IPO). Trier and Richter ( 2015 ) find that online organizational networking has many unbalanced multiplex relationships, mostly comprising of weak ties and temporal change. They attribute the uneven user contribution in social networking sites to discourse drivers and information retrievers. Schlagwein and Hu ( 2016 ) identify collaboration, broadcast, dialogue, sociability, and knowledge management as the social media types that serve varied organizational purposes. Claussen et al. ( 2013 ) study Facebook to conclude that social media networks can exercise management not only by excluding participants, but also by driving softer changes in incentive/reward systems.

Subramaniam and Nandhakumar ( 2013 ) study enterprise system users and find that integrating social media facilitates user interaction that helps embed relationship ties between virtual actors. Another study concerning social features in enterprise systems reveals that business interactions are less social, and highly context specific (Mettler and Winter 2016 ). Van Osch and Steinfield ( 2016 ) showed that the enterprise system user involved in social network posting will show differences in team boundary spanning activities based on their hierarchical position (leadership, team member, etc.). Benthaus et al. ( 2016 ) analyse Twitter data to find that social media management tools have a catalysing effect on employee output as they enrich the user engagement process. Gray et al. ( 2011 ) study the social bookmarking system to find that social diversity of information sources is a good predictor of employee innovativeness. Kuegler et al. ( 2015 ) show that using enterprise social networking within teams strongly influences task performance and employee innovativeness. Leonardi ( 2014 ) reveals that communication visibility increases meta-knowledge between organizations, which results in innovative products and services minus knowledge duplication. Aggarwal et al. ( 2012 ) interestingly reveal positive effects of negative employee posts on an organization’s reputation, given that such posts attract larger audience.

Miranda et al. ( 2015 ) suggest that diffusion of social media is based on an organization’s vision that offers a well-defined range of moves to choose from, with the freedom to improvise. Xu and Zhang ( 2013 ) regard Wikipedia as a social media platform and conclude that it improves information environment in the financial market and the value of information aggregation. Qiu et al. ( 2014 ) study prediction markets to find that users with increased social connections are less likely to invest in information acquisition from external sources. Miller and Tucker ( 2013 ) study the extent of social media managed by firms to report that most firm postings are centred on firm’s achievement and are not necessarily in clients’ interest. In summary, studies reviewed under this theme are focussed on analysing the impact of integrating social media within work roles in organizations. Effective management and utilization of social media is agreed to provoke employee activity, which helps in employee innovativeness, retention, and motivation. Studies also hint against ignoring social media engagement, which can reportedly have a negative impact on a company’s image.

4.3.4 Social Media for Marketing

Social media sites are now a huge part of marketing tactics, and the documented studies are a good showcase of the extent to which social media is being integrated in marketing strategies. García-Crespo et al. ( 2010 ) study the continuous interaction between customers and organizations, as it impacts the social web environment with implications for marketing and new product development. Goh et al. ( 2013 ) study the user and market generated content for engagement in social media brand community to find that it has a positive impact on purchase expenditures. Rishika et al. ( 2013 ) demonstrate how higher social media activity directly correlates with higher participation and customer patronage. Aggarwal and Singh ( 2013 ) find that blogs help managers with their products in the screening stage, and also offer leverage in negotiating better contract terms. Dou et al. ( 2013 ) research optimizing the strength of a network by adjusting the embedded social media features with the right market seeding and pricing strategies.

Oestreicher-Singer and Zalmanson ( 2013 ) reveal that the firms are more viable when they integrate social media in purchase and consumption experience, rather than using it as a substitute for soft online marketing. Lee et al. ( 2015b ) study the importance of social commerce in marketplace to find that Facebook likes increase sales, drive traffic, and introduce socialization in the shopping experience. Xie and Lee ( 2015 ) scan purchase records on Facebook to find that exposure to owned and earned social media activities positively impacts consumers’ likeliness to purchase brands. Chen et al. ( 2015 ) study music sales on MySpace to find that broadcasting, timing and content of the personal message has significant effect on sales. Qiu et al. ( 2015 ) study YouTube data to find that learning and network mechanisms statistically and economically impact video views. Sung et al. ( 2016 ) use Facebook data of universities and colleges across the US to show that people in the same class year or same major tend to form denser groups/networks. In a slightly different study, Oh et al. ( 2016 ) investigate the pricing models for an online newspaper, and find that charging for previously free online content has a disproportionate impact on word of mouth for niche and popular topics/articles. Susarla et al. ( 2016 ) find that social media initiatives succeed when a sustained conversation with likely adopters is maintained.

Studies within this theme focus on the role of community structure and structural patterns in using social media for marketing purposes. For successful social media implementation, it is important to effectively incorporate social computing with content delivery in the digital content industry with growing user population. Most studies identify meaningful conversations with customers as an important attribute of social media marketing. Also, identifying specific customer segments across social media site, for instance, members of a forum/group or organization, helps e-marketers to target specific customers based on demographic patterns and similar interests.

4.3.5 Social Media and Participation in Online Communities

There are many facets to developing and maintaining an online community, and user participation plays an integral role in it. Ray et al. ( 2014 ) identify that user engagement increases user intention to revisit an online community. Singh et al. ( 2014 ) analyse employee blog reading behaviour and show how reader attraction and retention are influenced by textual characteristics that appeal to reader sentiments. Butler and Wang ( 2012 ) find that changing content in an online discussion community affects member dynamics and community responsiveness, both positively and negatively. An early study on participation in online communities finds that different community commitments impact behaviours differently (Bateman et al. 2011 ). Chau and Xu ( 2012 ) develop a framework capable of gathering, extracting, and analyzing blog information that can be applied to any organization, topic, or product/service.

Goes et al. ( 2016 ) study goal setting and status hierarchy theories to find that glory-based incentives motivate users to contribute more user-generated content only before/until the goal is reached, with the contribution dropping significantly later. Khansa et al. ( 2015 ) examine Yahoo! Answers, and find that artefacts like incentives, membership tenure, and habit or past behaviour hugely influence active online participation. Tang et al. ( 2012 ) examine the concept of incentives on social media, particularly YouTube, for content contribution and find that a user is driven to contribute on social media based on their desire for revenue sharing, exposure, and reputation. Zhang and Wang ( 2012 ) use economic and social role theories in a Wikipedia context to show that in a collaborative network, the editor determines the total contribution towards collaborative work. Dennis et al. ( 2016 ) create a theoretical framework for corporate blogs and analyse Fortune 500 companies to find that a blog’s target audience and the alignment of blog content and its management significantly impact the visibility of that blog. Most of the studies under this theme focus on analyzing data on blogs. They highlight the importance of word of mouth, which is closely associated with user satisfaction. It also emerges from these studies that user engagement and consequent satisfaction play parallel and mediating roles within such online communities.

4.3.6 Risks and Concerns with the Use of Social Media

Social media and its associated risks have captured the attention of many authors. A very early study by Griffiths and Light ( 2008 ) focuses on the problem of media convergence, whereby a gaming website includes social media features, putting vulnerable young audience at the risk of scamming. An Australian study suggests that many users are unaware of the potential risks of disclosing personal information on social media site, or consider themselves as low risk targets (Tow et al. 2010 ). Krasnova et al. ( 2010 ) find that the ease of forming and maintaining relationships on an enjoyable social platform motivates users to disclose personal information. Their study shows that user trust in a service/network provider, and privacy control options on a networking site greatly dismiss user perceptions of associated risk. Vishwanath ( 2015 ) finds that farcing attacks on Facebook occur at two levels – victim to phishers with phony profiles and victim to phishers soliciting personal information directly from them.

To combat the privacy problem of photos, videos, and other content posted online, Fogués et al. ( 2014 ) developed a Best Friend Forever tool that automatically distinguishes friends on a user’s profile by assigning individual values based on relationship ties. Zhang et al. ( 2016 ) find that incorporating non-verbal features of reviewers can massively improve the performance of online fake review detection models. Gerlach et al. ( 2015 ) find that user perception of privacy risks has a mediating effect on the relationship between policy monetization and user willingness to share information. Burtch et al. ( 2016 ) analyse a large online crowd funding platform and report that when campaign contributors control/conceal visibility from public display, there is a negative impact on subsequent visitor’s conversion likelihood and average contributions. In a different study, Choi et al. ( 2015 ) find that information dissemination and network commonality has a high impact on individual’s perception of privacy invasion and relationship bonding that impedes transactional and interpersonal avoidances.

Studies reviewed here discuss a social contagion effect of risks associated with social media use. Recent studies (2014–2016) suggest educating audiences about the threats associated with the extent of personal information being disclosed on social media sites. They recommend government agencies to keep the users informed, and the social media sites to control some of their security features. It is necessary to define and control privacy settings across these many existing social networks.

4.3.7 Negative Stigma Attached to Social Media Use

Some studies suggest that there is a negative stigma associated with the use of social media in the workplace. In a typical case study, Koch et al. ( 2012 ) analyze three employee layers in an organization to find that new hires (users of social media sites) showed improved morale and employee engagement, some middle managers (non users) were frustrated and experienced isolation, while the senior execs were wary of social media use. In a contrasting case, Cao et al. ( 2015 ) suggest that social media has the potential to build employees’ social capital to positively influence their knowledge integration. In discussing the impact of social media on organizational life, Koch et al. ( 2013 ) find that conflicts can stem between workplace values and the values these employees ascribe to social media.

In a gender-based study on social network facilitated team collaboration, Shen et al. ( 2010 ) found that the collective intention in men was influenced by positive emotions, attitude and group norms, while the collective participation intention in women was affected by negative emotions and social identity. Huang et al. ( 2015 ) debate the concept of communicational ambidexterity to understand the conflicting demands of managing internal organization communication in contrast to open and distributed social media communication. Wu ( 2013 ) suggests information-rich networks enabled by social media tend to drive job security and employee performance. Lu et al. ( 2015 ) use the social network theory to conclude that structural and cognitive dimensions of social relationships positively impact job performance. Ali-Hassan et al. ( 2015 ) show social and cognitive use of social media has a positive influence on employee performance, while hedonic use of social media leaves a negative impact on routine performance.

These reviewed studies showcase that social networking encourages shared language and trust between employees in a workspace. Another emerging suggestion highlights that organizations should exercise policy, and use socialization and leadership-based mechanisms to counter any problems resulting from differing workplace values. Some of these studies show interest in the cognitive side of social ties that positively nurture social relationships and innovation performance.

4.3.8 Social Media and Value Creation

Studies in the extant literature have particularly focussed on the aspect of value creation within online communities. As Ridings and Wasko ( 2010 ) have observed, an online discussion group/community is a direct product of its social and structural dynamics. Porter et al. ( 2013 ) investigate firm value and find that a sponsor’s efforts are stronger with positive and direct effect on trust building. Luo et al. ( 2013 ) suggest that social media has faster predictive value than conventional online media, and that the embedded metrics like consumer ratings are leading indicators of a firm’s equity. Hu et al. ( 2015 ) develop a formative model with an aggregate online social value construct and identify factors to increase user benefits and satisfaction, ensuring customer retention via continued usage of online services. In a public organization study focussing on social networking system, Karoui et al. ( 2015 ) suggest that differing perceptions of social capital can result in actors adopting differing strategies for holding their social position within an organization. Barrett et al. ( 2016 ) find that value creation in online communities expands beyond the dyadic relationship between a firm and the community to include a more intricate relationship involving stakeholders of a wider ecosystem. Dong and Wu ( 2015 ) use data from Dell and Starbucks and find substantial evidence for online user innovation-enabled implementation increasing firm value. Overall, the studies on social media and value creation emphasize on influence of social and structural interplay on sustainability, which is visible over longitudinal examination of their relationship to one another.

4.3.9 Role of Social Media During Critical/Extreme Events

Certain authors are more interested in micro-blogging used at the time of critical/extreme events. In an attempt to filter real time news/updates from irrelevant personal messages and spam, Cheng et al. ( 2011 ) propose analysis of information diffusion patterns for a large set of micro-blogs that update emergency news. They claim that their approach (using Twitter data) outperforms other benchmark solutions to offer diverse user preferences and customized results during critical events. Cheong and Lee ( 2011 ) use Twitter data to propose a framework that is useful for Homeland Securities and Law enforcement agencies to record and respond to terror situations. Oh et al. ( 2013 ) also study Twitter data from three extreme events to find that information without any clear source is at the top, personal involvement comes second, with anxiety at third place in the list of rumour causing factors during social crisis events. Wang et al. ( 2014 ) affirm that news spreads widely through online portals. They find that news first posted even on a small news portal can be picked and reposted by a major news portal, forming a hotspot event for the news to rapidly spread over the Internet.

Lee et al. ( 2015a ) performed negative binomial analysis of the 2013 Boston marathon tragedy Tweets to find that follower numbers, reaction time, and hash tagging significantly affected the diffusion of Tweets. Oh et al. ( 2015 ) analysed Twitter data from the 2011 Egypt revolution and found that hash tags played a critical role in gathering information and maintaining situational awareness during such politically unstable phases. Ling et al. ( 2015 ) undertake a qualitative study of 2011 Thailand flooding data to conclude that social media can offer a community: structural, resource, and psychological empowerment to achieve collaborative control and collective participation. In summary, studies since 2011 have been particularly examining Twitter data, and have derived significant insights on their positive effect during critical/extreme events.

4.3.10 Social Media for Help/Support

Some users post updates on social media with an aim to seek help/support from online communities. Maier et al. ( 2015a ) find that such posts cause social overload for other users, and the psychological consequences include feelings of exhaustion, low user satisfaction, and high intentions of reducing/stopping the use of social media sites. Yan et al. ( 2015a ) find that healthcare traits of patients help them establish social connections online, which is influenced by their cognitive abilities. Spagnoletti et al. ( 2015 ) develop a user utility model for integrating social media in personalized elderly healthcare that is capable of challenging traditional organizational boundaries to transform the internal and external stakeholder engagement. Yan and Tan ( 2014 ) propose a partially observed Markov decision process model to find sufficient evidence suggesting emotional support is most significant in improving patient health. Kallinikos and Tempini ( 2014 ) study the ups and downs of having a large unsupervised social network based on patient self-reporting for gathering and examining data on patients’ health.

Limited number of studies has been recorded for this theme. These studies are fairly recent suggesting a new emerging trend, where health/support based communities are being formed. The expanse of such communities seems to be largely dependent on the information processing capacity and the range of social ties that the members of such networks can handle. Using social media to bring together people with similar health conditions suggests that informational and social support can have varying influence on patient health.

4.3.11 Public Bodies and Social Media Interaction

User-generated content from social media is becoming one of the important information channels across public administrative bodies and political contexts. Baur ( 2017 ) has developed a MarketMiner framework that massively improves the utilization of multi-source, multi-language social media content, which can be applied to areas such as open government. Rosenberger et al. ( 2017 ) use abstraction-based modelling to conceptualize the data structure, and conclude that wrapping social network application programming interfaces allow mutual integration of most user activities. Gonzalez-Bailon et al. ( 2010 ) show that political discussions in online networks are larger and deeper compared to other networks. Ameripour et al. ( 2010 ) analyse the restricted Iranian social networks, subject to surveillance and censorship to find that Internet conviviality is not an independent variable with deterministic outcomes, but is a technology shaped by economic and political forces. Although, not published in the list of journals included in this review, Kapoor and Dwivedi ( 2015 ) provided a detailed discussion on how social media was used intensively to transform electoral campaigns during India’s last general election. Similar use has also been reported in other contexts (for example, US presidential elections) by other studies.

Except one study (that is, Ameripour et al. 2010 ), the remaining reviewed under this category are very recent (2015–2016). These studies suggest the use of social media for increasing public engagement and transparency. Most of these studies used technical frameworks and modelling techniques to identify communication clusters and structures to derive insights relevant to open government and political campaigns.

4.3.12 Traditional v/s Social Media

Another set of studies investigate the differences between traditional and social media. A very early study by Wattal et al. ( 2010 ) compares the big money tactics for political campaigning with social media campaigning to reveal that Internet and the blogosphere can majorly influence campaigning and election results. Dewan and Ramaprasad ( 2014 ) examine the importance of new and old media within the music industry; they find radio positively and consistently affecting sales of songs and albums, and sales displacement from free online sampling overpowering positive word of mouth on sales. Miranda et al. ( 2016 ) compare traditional and social media to suggest that there are evils associated with the societal benefits of social media, and mass media has a detrimental effect on public discourse.

4.3.13 Testing Pre-Established Models

Some studies in literature restrict focus to pre-established models and relationships for evaluating varied aspects of social media. Fang et al. ( 2013 ) apply social network theories to suggest positive social influence on adoption probabilities. Levina and Arriaga ( 2014 ) use Bourdieu’s theory to explain the role of status markers and external sources in shaping social dynamics. Bharati et al. ( 2014 ) combine institutional theory and organizational innovation, whereby institutional pressures significantly predict absorptive capacity. Kekolahti et al. ( 2015 ) use Bayesian networks to indicate the decrease in perceived importance of communication with increase in age. Chang et al. ( 2015 ) integrate social distance with clustering methods to show shorter social distance results in satisfactory trust. Chung et al. ( 2017 ) employ the Technology Acceptance Model, and find positive effects between traveller readiness and ease of using geo-tagging. Zhao et al. ( 2016 ) use theory of planned behaviour and attribution theory to find that virtual rewards for sharing knowledge online undermine enjoyment. Yu et al. ( 2015 ) use the causation and heuristic theories to find that affect influences self disclosure indirectly by adjusting perceived benefits. Stanko ( 2016 ) employs Innovation Diffusion Theory, and finds that community interaction influences innovations that are used to aid a further innovation.

5 Discussion

In reviewing the publications gathered for this paper, commonalities have been observed in the myriad aspects of social media chosen for investigation. While many studies focussed their attention on understanding the behaviours of social media users, the others examined entrepreneurial participation and firm behaviour. A number of studies have focussed on the content being posted in online communities, several of which report on the repercussions of some of this content being used as an awareness medium during critical events and tragedies. Interesting revelations were made by authors studying the use of social media as a platform to render and/or receive help or support, and its incorporation in the field of healthcare and public administration. Value creation and the ill-effects associated with the use of social media at the workplace were also discussed. Several studies chose to test previously established hypotheses and models, while others compared traditional media with social media. Prior research has also provided insights into how firms have been using social media to market their products and services. These strategies run in parallel with the reviews and recommendations posted by users on social media sites, which have also received considerable attention in the literature. In summary, given that different types of social media platforms are emerging, and different consequences are associated with their use, research in this field will continue to evolve. This is also evidenced by the increased number of publications related to usage and impact over the past five years.

Social media platforms have essentially redefined the ways in which people choose to communicate and collaborate. An online community is a socio-technological space where a sense of communal identity drives engagement, which, in turn, enhances satisfaction (Ray et al. 2014 ). Intriguingly, social media are facilitating the emergence of virtual knowledge communities and self help networks. These web-based arrangements allow medical practice and research to access patient experience on a daily basis, which was not possible earlier. However, since research in this area is still in its early stages, it is difficult to assess the social complexity involved (e.g., stability of a networking platform that brings together patients with medical experts) in the process (Kallinikos and Tempini 2014 ).

Firms are recognizing social media as a prominent indicator of equity value that not only improves short-term performance, but also brings about long-term productivity benefits (Luo et al. 2013 ). The reviewed studies suggest that incorporatin social media in firms increases meta-knowledge (who’s who in an organization and who does what), which helps avoid knowledge duplication and promotes new ways of managing work (Leonardi 2014 ). Active management of social media has been observed to be more effective when those inside rather than outside a firm are engaged (Miller and Tucker 2013 ).

A specific line of research focuses on consumers, who substantially rely on online reviews before making any purchase decision. The research papers reviewed in this study exhibit diversity in studying authenticity of reviews for travel sites, social bookmarking and review sites, movie ratings, car manufacturing, and social media check-ins. Studies concur that there has been an exponential increase in the number of fake reviews, which is severely damaging the credibility of online reviews and putting business values at risk (Zhang et al. 2016 ). Some studies have also empirically identified consumers’ social media participation as a key metric contributing to the profitability of a business (Rishika et al. 2013 ). There evidently exists a direct correlation between consumer engagement on social media sites and their shopping intentions, which makes the issue of legitimate reviews all the more important for businesses and consumers. Although some studies have proposed models and algorithms that claim to filter authentic reviews from the rest, there is no single and straightforward solution reported yet that can fully combat this problem.

The issue of negative posts has received considerable attention in the literature. Prior research suggests that, overall, the impact of negative posts or electronic word of mouth is much higher than the positive ones that increase readership (Aggarwal et al. 2012 ). This problem is also prevalent in organizations. According to the studies reviewed here, organizations either prohibit employees from posting controversial content online, or employees themselves refrain from doing so, fearing negative repercussions. The same employees also share positive posts, and the adverse effect of the few negative posts is offset by positive ones. It is in a firm’s interests to encourage free will enterprise blogging to break down knowledge silos and yield higher employee productivity (Singh et al. 2014 ).

Businesses looking to monetize online content and social search rely heavily on substantial understanding of consumer behaviour in terms of their interaction and participation in social settings (Susarla et al. 2012 ). As consumers gain access to social platforms that offer free content consumption without an obligatory payment, the relationship between sampling and sales becomes all the more important (Dewan and Ramaprasad 2014 ). There is much research supporting the belief that online word of mouth has a critical role to play in a firm’s overall performance, and introducing a pay-wall (for previously free content) can significantly reduce the volume of word of mouth for popular content in comparison to niche content (Oh et al. 2016 ). Determining consumers’ social influence in an online community is of critical interest to managers, who seek to gain some leverage from the potential of social media (Shi et al. 2014 ). Some researchers find it difficult to distinguish social influence from users’ self selection preferences. From an analysis point of view, it then becomes necessary to separate factors affecting user membership in a social network from various types of social influence (Susarla et al. 2012 ).

The findings on the use of social media in emergencies suggests that a general user response in an online community is very different from that during a crisis, as those responses then become more reflexive. It has been observed that in times of crisis, lack of information sources coupled with too many situation reports being shared by the users of a social media platform can precipitate a rumour mill. It thus becomes incumbent on emergency responders to release reliable information, whilst trying to control collective anxiety in the community, to suppress the rumour threads (Oh et al. 2013 ). Furthermore, security concerns are increasingly common with involuntary online exposure on social media, and research on this subject suggests that information dissemination with network commonality affects privacy invasion and user bonding (Choi et al. 2015 ). It has been learnt that an individual’s or firm’s decision to withhold information in the interest of privacy can have both positive and negative effects on their utility (Burtch et al. 2016 ).

In reviewing the 132 publications on social media and social networking, it was observed that many studies relied primarily on social exchange theory, network theory and organization theory. Table  3 , shown below, lists other theories that have been used by at least two publications. There were several other theories that were used by at least once, including social role theory, game theory, structural holes theory, management and commitment theories, institutional theory, deterrence and mitigation theories, and self determination and self categorization theories. It is noteworthy that dominant IS adoption theories such as Unified Theory of Acceptance and Use of Technology (Dwivedi et al. 2017b , c ; Rana et al. 2017 ; Venkatesh et al. 2003 ), Technology Acceptance Model (Davis 1989 ) and Innovation Diffusion Theory (Kapoor et al. 2015 ) are less widely utilised.

In addition, our review of the literature on social media identified dominant research methods employed by scholars. Qualitative, quantitative, and mixed methods were used by most of these studies. Closer scrutiny of the 132 publications reviewed in this study revealed the multitude of techniques applied for gathering data. Quantitative methods employed in these studies mostly adopted analytical techniques and surveys (Table  4 ). On the other hand, publications using qualitative methods mainly used case studies and interviews to gather the required data (Table 4 ). As expected, studies employing mixed methods used a combination of analytical and conceptual techniques, alongside surveys and content analysis (Table 4 ). Table 4 summarizes the various research approaches used by publications in our corpus.

The reviewed publications were also analyzed to determine the nature of the social network that were studied. Precisely 46 websites emerged, with Facebook, online communities, Twitter, Blogs and YouTube being most frequently targeted. Networks analysed by at least two or more studies have been identified in Table  5 . The other networks that received attention from the reviewed publications include Ebay, Flickr, Flixster, Gtalk, microsoft, MSN Space, Patientslikeme, New York Times, TripAdvisor.com , and Boxofficemojo.com . Studies also focussed on websites related to online news, Q&A websites, discussion groups and forums, online radio and television, and medical sites such as Webmd.com .

5.1 Limitations and Future Research Directions

Studies, such as the one by Cheung et al. ( 2012 ), that examine aspects of popular websites, warn against consumer perceptions being under the influence of brand equity of those websites. They recommend exercising caution while generalizing such findings in the context of other websites (Cheung et al. 2012 ). Rosenberger et al. ( 2017 ) identify a similar problem with relying on publicly available data, in that the underlying abstraction makes findings valid only for the specific social media site that was analyzed, whilst significantly restricting its generalizability to other sites. In a similar vein, other studies (Krasnova et al. 2015 ; Khan and Jarvenpaa 2010 ; Tow et al. 2010 ) have acknowledged the limitation of restricting their research to a single social media site, and recommend future researchers to adopt a cross-platform perspective for drawing significant inferences.

Mettler and Winter ( 2016 ) suggest that there is a paucity of studies on Enterprise Social Systems because of its novelty, and urge researchers to fill this void. Turel and Serenko ( 2012 ) identify the lack of conceptualization in the notion of technology addiction; they recognize that the process of defining it is still in the early stages, and is being debated across communities. For researchers interested in examining aspects of Twitter, Cheng et al. ( 2011 ) recommend incorporating the location metric focused on Twitter’s geo location feature allowing users to trace the latitude and longitude of Tweets. Another recommendation for Twitter related studies comes from Benthaus et al. ( 2016 ), where they suggest researchers should study user involvement differently, based on how often users choose to ‘like’ the content of a company. As for use of social media for marketing in firms, the literature has restricted focus to the resulting marketing benefits, with limited evidence supporting the effectiveness of social platforms for enhancing employee communications (Miller and Tucker 2013 ).

For behavioural studies, researchers need to be wary of the fact that motivation for users to adopt social media is different, often contingent on their culture (Chiu and Huang 2015 ; Shen et al. 2010 . It is also important to note that behavioural reactions are susceptible to change over time, and changing habits have a role to play (Chiu and Huang 2015 ). Longitudinal research is thus always expected to offer a better understanding of the research problem when the intended behavioural reactions transfer into behaviour with time (Maier et al. 2015a ). In studying online reviews and recommendations, researchers can assume that these reviews are independent of one another and remain static over time; however, Zhang and Piramuthu ( 2016 ) suggest that this may not be true and future researchers should now concentrate on how this has evolved, and if herding behaviour exists on such online platforms. In studying behaviours, it has also emerged that users develop discontinuance intentions after continuance intentions, with the latter never being completely replaced by the former. Turel ( 2015 ) thus recommends studying the initiation of discontinuance intentions, whilst identifying the factors leading to its dominance and actual discontinuance attempts.

Matook et al. ( 2015a ) identify that there is a need to study the aspect of trust formation between individuals on social media, where no personal relationships exist (unlike sites such as Facebook). Chung et al. ( 2017 ) identify that researchers often associate the use of certain social media with young users (for instance, Maier et al. 2015b ), and fail to study the usage perceptions across various ages (Vishwanath 2015 ). Van Osch and Steinfield ( 2016 ) suggest that future researchers should explore the potential of Enterprise Social Media to gain insights into the tools that support disentanglement of team boundary spanning. Finally, researchers have established that the lifecycle of information and communication technologies tend to be emancipatory in their infancy but eventually evolve into hegemonic tools. They warn social media policymakers to be wary of reproducing this pattern with digital media; the recommendation is to involve more citizens in the development of Internet governance framework, rather than resting decisions with the members of political or economic power (Miranda et al. 2016 ).

6 Conclusions

This paper discusses the findings of 132 publications contributing to the literature on social media. Multiple emergent themes in this body of literature have been identified to enhance understanding of the advances in social media research. By building on empirical findings of previous social media research, many new studies have been successful in theorizing the nature of most social media platforms. User-generated content allows collective understanding, which is a massive machine-human knowledge processing function capable of managing chaotic volumes of information. Some key conclusions relevant to stakeholders, including researchers, have been identified here.

Social media technologies are no longer perceived just as platforms for socialization and congregation, but are being acknowledged for their ability to encourage aggregation.

In reviewing the 132 publications on social media and social networking, it was observed that most studies used social exchange theory, network theory and organization theory to support their studies.

Facebook, online communities, and twitter are the three most popular networks targeted by publications in the field of social media research.

Publications in 2011 were still reporting user-generated content as a new type of online content. However, the last six years have seen tremendous scholarly progression in discussing the many applications of social networking, highlighting the highs and lows associated with its use.

Majority of the publications reviewed in this study are focussed on behavioural side of social media, reviews, and integration of social media for marketing and organizational purposes.

Many publications in the year 2013 concentrated their efforts in investigating the very popular aspect of reviews and recommendations on social networks.

Publications have become more focussed on studying social commerce across networking sites, particularly, Facebook, MySpace, YouTube and so on between 2015 and 2016.

Publications have not shown much interest in support-seeking posts and negative stigma attached to social media use after the year 2015.

Most studies unanimously acknowledge social media for its information sharing and information exchange capabilities, with a focussed group of studies recognizing its effectiveness during natural disasters and critical events.

Almost all publications studying information sharing during natural disasters and critical events focus on Twitter data.

Publications on administration and political contexts were particularly found in 2010 and 2016, with no interest expressed in these contexts between 2011 and 2015.

With information systems now expanding beyond organizational peripheries to become a part of the larger societal context, it is important for strategic information systems research to delve into the competitive setting of dynamic social systems. Online communities are introducing extrinsic rewards that do not limit users’ intrinsic motivations. Research on such communities should expand to study the interplay between extrinisic and intrinsic rewards, particularly in terms of their ability to cultivate and sustain users’ intrinsic motivations. From an organizational perspective, research on social media should move past the conventional dyadic view of the relationship between an online community and a firm, and focus on reconceptualising online users as an ecosystem of stakeholders. Social media has re-established the dynamics between organizations, employees, and consumers. Given the rise in number of publications focussing on workplace setting since 2014, future researchers should aim to analyze stakeholders’ potential in adopting social media tools to successfully accomplish their work goals. As for the limitations of this collective review, publications reviewed here were limited to only nine journals. This potentially means studies with significant contributions to social media literature published in other journals have been overlooked. Future researchers can look to overcome such exclusions and focus on the overall review of literature on social media platforms. Future reviews may focus on reviewing articles published in a larger number of IS journals related to a specific type of social media (i.e. social networking sites, blogs), or specific issues related to social media use, such as information load, stress, and impact on productivity. Despite these limitations, our study provides a comprehensive and robust intellectual framework for social media research that would be of value to adacemics and practitioners alike.

TITLE: (“Social Media” or “social networking” or “facebook” or “linkedin” or “instagram” or “twitter”)

Refined by: DOCUMENT TYPES: (ARTICLE OR PROCEEDINGS PAPER)

Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI.

http://www.springer.com/business+%26+management/business+information+systems/journal/10796

Aggarwal, R., & Singh, H. (2013). Differential influence of blogs across different stages of decision making: The case of venture capitalists. MIS Quarterly, 37 (4), 1093–1112.

Article   Google Scholar  

Aggarwal, R., Gopal, R., Sankaranarayanan, R., & Singh, P. V. (2012). Blog, blogger, and the firm: Can negative employee posts lead to positive outcomes? Information Systems Research, 23 (2), 306–322.

AlAlwan, A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social Media in Marketing: A review and analysis of the existing literature. Telematics and Informatics Available at http://www.sciencedirect.com/science/article/pii/S0736585317301077 .

Ali-Hassan, H., Nevo, D., & Wade, M. (2015). Linking dimensions of social media use to job performance: The role of social capital. The Journal of Strategic Information Systems, 24 (2), 65–89.

Ameripour, A., Nicholson, B., & Newman, M. (2010). Conviviality of internet social networks: An exploratory study of internet campaigns in Iran. Journal of Information Technology, 25 (2), 244–257.

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers' objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.

Barrett, M., Oborn, E., & Orlikowski, W. (2016). Creating value in online communities: The sociomaterial configuring of strategy, platform, and stakeholder engagement. Information Systems Research, 27 (4), 704–723.

Bateman, P. J., Gray, P. H., & Butler, B. S. (2011). Research note-the impact of community commitment on participation in online communities. Information Systems Research, 22 (4), 841–854.

Baur, A. W. (2017). Harnessing the social web to enhance insights into people’s opinions in business, government and public administration. Information Systems Frontiers, 19 (2), 231–251.

Benthaus, J., Risius, M., & Beck, R. (2016). Social media management strategies for organizational impression management and their effect on public perception. The Journal of Strategic Information Systems, 25 (2), 127–139.

Bharati, P., Zhang, C., & Chaudhury, A. (2014). Social media assimilation in firms: Investigating the roles of absorptive capacity and institutional pressures. Information Systems Frontiers, 16 (2), 257–272.

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55 (4), 77–84.

Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2011). Trust perceptions of online travel information by different content creators: Some social and legal implications. Information Systems Frontiers, 13 (2), 221–235.

Burtch, G., Ghose, A., & Wattal, S. (2016). Secret admirers: An empirical examination of information hiding and contribution dynamics in online Crowdfunding. Information Systems Research, 27 (3), 478–496.

Butler, B. S., & Wang, X. (2012). The cross-purposes of cross-posting: Boundary reshaping behavior in online discussion communities. Information Systems Research, 23 (3-part-2), 993–1010.

Cao, X., Guo, X., Liu, H., & Gu, J. (2015). The role of social media in supporting knowledge integration: A social capital analysis. Information Systems Frontiers, 17 (2), 351–362.

Centeno, R., Hermoso, R., & Fasli, M. (2015). On the inaccuracy of numerical ratings: Dealing with biased opinions in social networks. Information Systems Frontiers, 17 (4), 809–825.

Chang, I., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24 (4), 323–346.

Chang, W. L., Diaz, A. N., & Hung, P. C. (2015). Estimating trust value: A social network perspective. Information Systems Frontiers, 17 (6), 1381–1400.

Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS Quarterly, 36 (4), 1189–1216.

Google Scholar  

Chen, R., & Sharma, S. K. (2015). Learning and self-disclosure behavior on social networking sites: The case of Facebook users. European Journal of Information Systems, 24 (1), 93–106.

Chen, J., Xu, H., & Whinston, A. B. (2011). Moderated online communities and quality of user-generated content. Journal of Management Information Systems, 28 (2), 237–268.

Chen, A., Lu, Y., Chau, P. Y., & Gupta, S. (2014). Classifying, measuring, and predicting users’ overall active behavior on social networking sites. Journal of Management Information Systems, 31 (3), 213–253.

Chen, H., De, P., & Hu, Y. J. (2015). IT-enabled broadcasting in social media: An empirical study of artists’ activities and music sales. Information Systems Research, 26 (3), 513–531.

Cheng, J., Sun, A., Hu, D., & Zeng, D. (2011). An information diffusion-based recommendation framework for micro-blogging. Journal of the Association for Information Systems, 12 (7), 463–486.

Cheong, M., & Lee, V. C. (2011). A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via twitter. Information Systems Frontiers, 13 (1), 45–59.

Cheung, C. M. Y., Sia, C. L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13 (8), 618.

Chiu, C. M., & Huang, H. Y. (2015). Examining the antecedents of user gratification and its effects on individuals’ social network services usage: The moderating role of habit. European Journal of Information Systems, 24 (4), 411–430.

Choi, B. C., Jiang, Z., Xiao, B., & Kim, S. S. (2015). Embarrassing exposures in online social networks: An integrated perspective of privacy invasion and relationship bonding. Information Systems Research, 26 (4), 675–694.

Chung, N., Tyan, I., & Han, H. (2017). Enhancing the smart tourism experience through geotag. Information Systems Frontiers, 19 (4), 731–742.

Claussen, J., Kretschmer, T., & Mayrhofer, P. (2013). The effects of rewarding user engagement: The case of facebook apps. Information Systems Research, 24 (1), 186–200.

Culnan, M. J. (1986). The intellectual development of management information systems, 1972-1982: A co-citation analysis. Management Science, 32 (2), 156.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 318–339.

Dellarocas, C., Gao, G., & Narayan, R. (2010). Are consumers more likely to contribute online reviews for hit or niche products? Journal of Management Information Systems, 27 (2), 127–158.

Dennis, A. R., Minas, R. K., & Lockwood, N. S. (2016). Mapping the corporate blogosphere: Linking audience, content, and management to blog visibility. Journal of the Association for Information Systems, 17 (3), 162.

Dewan, S., & Ramaprasad, J. (2014). Social media, traditional media, and music sales. MIS Quarterly, 38 (1), 101–121.

Dong, J. Q., & Wu, W. (2015). Business value of social media technologies: Evidence from online user innovation communities. The Journal of Strategic Information Systems, 24 (2), 113–127.

Dou, Y., Niculescu, M. F., & Wu, D. J. (2013). Engineering optimal network effects via social media features and seeding in markets for digital goods and services. Information Systems Research, 24 (1), 164–185.

Dwivedi, Y. K., Kapoor, K. K., & Chen, H. (2015). Social media marketing and advertising. The Marketing Review, 15 (3), 289–309.

Dwivedi, Y. K., Rana, N. P., & Alryalat, M. (2017a). Affiliate marketing: An overview and analysis of emerging literature. The Marketing Review, 17 (1), 33–50.

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017b). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers , 1–16. Available at. https://doi.org/10.1007/s10796-017-9774-y .

Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017c). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34 (2), 211–230.

Fang, X., Hu, P. J. H., Li, Z., & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24 (1), 128–145.

Fogués, R. L., Such, J. M., Espinosa, A., & Garcia-Fornes, A. (2014). BFF: A tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers, 16 (2), 225–237.

García-Crespo, Á., Colomo-Palacios, R., Gómez-Berbís, J. M., & Ruiz-Mezcua, B. (2010). SEMO: A framework for customer social networks analysis based on semantics. Journal of Information Technology, 25 (2), 178–188.

Garg, R., Smith, M. D., & Telang, R. (2011). Measuring information diffusion in an online community. Journal of Management Information Systems, 28 (2), 11–38.

Gerlach, J., Widjaja, T., & Buxmann, P. (2015). Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. The Journal of Strategic Information Systems, 24 (1), 33–43.

Goes, P. B., Lin, M., & Au Yeung, C. M. (2014). “Popularity effect” in user-generated content: Evidence from online product reviews. Information Systems Research, 25 (2), 222–238.

Goes, P. B., Guo, C., & Lin, M. (2016). Do incentive hierarchies induce user effort? Evidence from an online knowledge exchange. Information Systems Research, 27 (3), 497–516.

Goh, K. Y., Heng, C. S., & Lin, Z. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information Systems Research, 24 (1), 88–107.

Gonzalez-Bailon, S., Kaltenbrunner, A., & Banchs, R. E. (2010). The structure of political discussion networks: A model for the analysis of online deliberation. Journal of Information Technology, 25 (2), 230–243.

Gray, P. H., Parise, S., & Iyer, B. (2011). Innovation impacts of using social bookmarking systems. MIS Quarterly, 35 (3), 629–643.

Greenwood, B. N., & Gopal, A. (2015). Research note—Tigerblood: Newspapers, blogs, and the founding of information technology firms. Information Systems Research, 26 (4), 812–828.

Griffiths, M., & Light, B. (2008). Social networking and digital gaming media convergence: Classification and its consequences for appropriation. Information Systems Frontiers, 10 (4), 447–459.

Gu, B., Konana, P., Raghunathan, R., & Chen, H. M. (2014). Research note—The allure of Homophily in social media: Evidence from investor responses on virtual communities. Information Systems Research, 25 (3), 604–617.

Hildebrand, C., Häubl, G., Herrmann, A., & Landwehr, J. R. (2013). When social media can be bad for you: Community feedback stifles consumer creativity and reduces satisfaction with self-designed products. Information Systems Research, 24 (1), 14–29.

Hu, T., Kettinger, W. J., & Poston, R. S. (2015). The effect of online social value on satisfaction and continued use of social media. European Journal of Information Systems, 24 (4), 391–410.

Huang, J., Baptista, J., & Newell, S. (2015). Communicational ambidexterity as a new capability to manage social media communication within organizations. The Journal of Strategic Information Systems, 24 (2), 49–64.

Hwang, Y. C., Yuan, S. T., & Weng, J. H. (2011). A study of the impacts of positive/negative feedback on collective wisdom—Case study on social bookmarking sites. Information Systems Frontiers, 13 (2), 265–279.

Ismagilova, E., Dwivedi, Y. K., Slade, E. L., & Williams, M. D. (2017). Electronic word of mouth (eWOM) in the marketing context: A state of the art analysis and future directions . Cham: Springer International Publishing.

Book   Google Scholar  

Kallinikos, J., & Tempini, N. (2014). Patient data as medical facts: Social media practices as a foundation for medical knowledge creation. Information Systems Research, 25 (4), 817–833.

Kapoor, K. K., & Dwivedi, Y. K. (2015). Metamorphosis of Indian electoral campaigns: Modi’s social media experiment. International Journal of Indian Culture & Business Management, 11 (4), 496–516.

Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2015). Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service. Information Systems Frontiers, 17 (5), 1039–1056.

Kapoor, K. K., Dwivedi, Y. K., & Piercy, N. (2016). Pay-per-click advertising: A review of literature. The Marketing Review, 16 (2), 183–202.

Karoui, M., Dudezert, A., & Leidner, D. E. (2015). Strategies and symbolism in the adoption of organizational social networking systems. The Journal of Strategic Information Systems, 24 (1), 15–32.

Kekolahti, P., Karikoski, J., & Riikonen, A. (2015). The effect of an individual’s age on the perceived importance and usage intensity of communications services—A Bayesian network analysis. Information Systems Frontiers, 17 (6), 1313–1333.

Khan, Z., & Jarvenpaa, S. L. (2010). Exploring temporal coordination of events with Facebook. Com. Journal of Information Technology, 25 (2), 137–151.

Khansa, L., Ma, X., Liginlal, D., & Kim, S. S. (2015). Understanding members’ active participation in online question-and-answer communities: A theory and empirical analysis. Journal of Management Information Systems, 32 (2), 162–203.

Koch, H., Gonzalez, E., & Leidner, D. (2012). Bridging the work/social divide: The emotional response to organizational social networking sites. European Journal of Information Systems, 21 (6), 699–717.

Koch, H., Leidner, D. E., & Gonzalez, E. S. (2013). Digitally enabling social networks: Resolving IT–culture conflict. Information Systems Journal, 23 (6), 501–523.

Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social networks: Why we disclose. Journal of Information Technology, 25 (2), 109–125.

Krasnova, H., Widjaja, T., Buxmann, P., Wenninger, H., & Benbasat, I. (2015). Research note—Why following friends can hurt you: An exploratory investigation of the effects of envy on social networking sites among college-age users. Information Systems Research, 26 (3), 585–605.

Kreps, D. (2010). My social networking profile: Copy, resemblance, or simulacrum? A poststructuralist interpretation of social information systems. European Journal of Information Systems, 19 (1), 104–115.

Kuan, K. K., Hui, K. L., Prasarnphanich, P., & Lai, H. Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16 (1), 48.

Kuegler, M., Smolnik, S., & Kane, G. (2015). What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software. The Journal of Strategic Information Systems, 24 (2), 90–112.

Lee, J., Agrawal, M., & Rao, H. R. (2015a). Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013. Information Systems Frontiers, 17 (5), 997–1005.

Lee, K., Lee, B., & Oh, W. (2015b). Thumbs up, sales up? The contingent effect of Facebook likes on sales performance in social commerce. Journal of Management Information Systems, 32 (4), 109–143.

Leonardi, P. M. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information Systems Research, 25 (4), 796–816.

Levina, N., & Arriaga, M. (2014). Distinction and status production on user-generated content platforms: Using Bourdieu’s theory of cultural production to understand social dynamics in online fields. Information Systems Research, 25 (3), 468–488.

Ling, C. L. M., Pan, S. L., Ractham, P., & Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: Social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16 (3), 174.

Lister, M. (2017). 40 essential social media marketing statistics for 2017. Available at: http://www.wordstream.com/blog/ws/2017/01/05/social-media-marketing-statistics . Accessed 22 June 2017.

Lu, B., Guo, X., Luo, N., & Chen, G. (2015). Corporate blogging and job performance: Effects of work-related and nonwork-related participation. Journal of Management Information Systems, 32 (4), 285–314.

Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2014). The IQ of the crowd: Understanding and improving information quality in structured user-generated content. Information Systems Research, 25 (4), 669–689.

Lundmark, L. W., Oh, C., & Verhaal, J. C. (2016). A little Birdie told me: Social media, organizational legitimacy, and underpricing in initial public offerings. Information Systems Frontiers , 1–16. https://doi.org/10.1007/s10796-016-9654-x .

Luo, X., & Zhang, J. (2013). How do consumer buzz and traffic in social media marketing predict the value of the firm? Journal of Management Information Systems, 30 (2), 213–238.

Luo, X., Zhang, J., & Duan, W. (2013). Social media and firm equity value. Information Systems Research, 24 (1), 146–163.

Ma, X., Khansa, L., Deng, Y., & Kim, S. S. (2013). Impact of prior reviews on the subsequent review process in reputation systems. Journal of Management Information Systems, 30 (3), 279–310.

Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015a). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24 (5), 447–464.

Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015b). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25 (3), 275–308.

Massari, L. (2010). Analysis of MySpace user profiles. Information Systems Frontiers, 12 (4), 361–367.

Matook, S., Brown, S. A., & Rolf, J. (2015a). Forming an intention to act on recommendations given via online social networks. European Journal of Information Systems, 24 (1), 76–92.

Matook, S., Cummings, J., & Bala, H. (2015b). Are you feeling lonely? The impact of relationship characteristics and online social network features on loneliness. Journal of Management Information Systems, 31 (4), 278–310.

McCain, K. W. (1984). Longitudinal author Cocitation mapping: The changing structure of macroeconomics. Journal of the American Society for Information Science, 35 (6), 351–359.

McCallum, A. K. (2002). Mallet: A machine learning for language toolkit. From http://mallet.cs.umass.edu

Mettler, T., & Winter, R. (2016). Are business users social? A design experiment exploring information sharing in enterprise social systems. Journal of Information Technology, 31 (2), 101–114.

Miller, A. R., & Tucker, C. (2013). Active social media management: The case of health care. Information Systems Research, 24 (1), 52–70.

Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with social media: How cognitive structuring of organizing vision facets affects it innovation diffusion. MIS Quarterly, 39 (3), 591–614.

Miranda, S. M., Young, A., & Yetgin, E. (2016). Are social media emancipatory or hegemonic? Societal effects of mass media digitization. MIS Quarterly, 40 (2), 303–329.

Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29 (3), 319–336.

Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? A digital business strategy for content providers in the social age. MIS Quarterly, 37 (2), 591–616.

Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37 (2), 407–426.

Oh, O., Eom, C., & Rao, H. R. (2015). Research note—Role of social Media in Social Change: An analysis of collective sense making during the 2011 Egypt revolution. Information Systems Research, 26 (1), 210–223.

Oh, W., Moon, J. Y., Hahn, J., & Kim, T. (2016). Research note—Leader influence on sustained participation in online collaborative work communities: A simulation-based approach. Information Systems Research, 27 (2), 383–402.

Phang, C. W., Kankanhalli, A., & Tan, B. C. (2015). What Motivates Contributors vs. Lurkers? An Investigation of Online Feedback Forums. Information Systems Research, 26(4), 773–792

Plume, C. J., Dwivedi, Y. K., & Slade, E. L. (2016). Social Media in the Marketing Context: A state of the art analysis and future directions (1st ed.). Oxford: Chandos Publishing Ltd.

Porter, C. E., Devaraj, S., & Sun, D. (2013). A test of two models of value creation in virtual communities. Journal of Management Information Systems, 30 (1), 261–292.

Qiu, L., Rui, H., & Whinston, A. B. (2014). Effects of social networks on prediction markets: Examination in a controlled experiment. Journal of Management Information Systems, 30 (4), 235–268.

Qiu, L., Tang, Q., & Whinston, A. B. (2015). Two formulas for success in social media: Learning and network effects. Journal of Management Information Systems, 32 (4), 78–108.

Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens’ adoption of an electronic government system: Towards a unified view. Information Systems Frontiers, 19 (3), 549–568.

Ray, S., Kim, S. S., & Morris, J. G. (2014). The central role of engagement in online communities. Information Systems Research, 25 (3), 528–546.

Ridings, C., & Wasko, M. (2010). Online discussion group sustainability: Investigating the interplay between structural dynamics and social dynamics over time. Journal of the Association for Information Systems, 11 (2), 95.

Rishika, R., Kumar, A., Janakiraman, R., & Bezawada, R. (2013). The effect of customers' social media participation on customer visit frequency and profitability: An empirical investigation. Information Systems Research, 24 (1), 108–127.

Rosenberger, M., Lehrer, C., & Jung, R. (2017). Integrating data from user activities of social networks into public administrations. Information Systems Frontiers, 19 (2), 253–266.

Schlagwein, D., & Hu, M. (2016). How and why organisations use social media: Five use types and their relation to absorptive capacity. Journal of Information Technology, 32 (2), 194–209.

Shen, A. X., Lee, M. K., Cheung, C. M., & Chen, H. (2010). Gender differences in intentional social action: We-intention to engage in social network-facilitated team collaboration. Journal of Information Technology, 25 (2), 152–169.

Shi, Z., & Whinston, A. B. (2013). Network structure and observational learning: Evidence from a location-based social network. Journal of Management Information Systems, 30 (2), 185–212.

Shi, Z., Rui, H., & Whinston, A. B. (2014). Content sharing in a social broadcasting environment: Evidence from twitter. MIS Quarterly, 38 (1), 123–142.

Singh, P. V., Sahoo, N., & Mukhopadhyay, T. (2014). How to attract and retain readers in Enterprise blogging? Information Systems Research, 25 (1), 35–52.

Spagnoletti, P., Resca, A., & Sæbø, Ø. (2015). Design for Social Media Engagement: Insights from elderly care assistance. The Journal of Strategic Information Systems, 24 (2), 128–145.

Stanko, M. A. (2016). Toward a theory of remixing in online innovation communities. Information Systems Research, 27 (4), 773–791.

Statista. (2017). Most famous social network sites worldwide as of April 2017, Ranked by number of active users (in millions). Available at: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ . Accessed 22 June 2017.

Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29 (4), 217–248.

Subramaniam, N., & Nandhakumar, J. (2013). Exploring social network interactions in enterprise systems: The role of virtual co-presence. Information Systems Journal, 23 (6), 475–499.

Sung, Y. S., Wang, D., & Kumara, S. (2016). Uncovering the effect of dominant attributes on community topology: A case of facebook networks. Information Systems Frontiers , 1–12. https://doi.org/10.1007/s10796-016-9696-0 .

Susarla, A., Oh, J. H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23 (1), 23–41.

Susarla, A., Oh, J. H., & Tan, Y. (2016). Influentials, Imitables, or Susceptibles? Virality and word-of-mouth conversations in online social networks. Journal of Management Information Systems, 33 (1), 139–170.

Tang, Q., Gu, B., & Whinston, A. B. (2012). Content contribution for revenue sharing and reputation in social media: A dynamic structural model. Journal of Management Information Systems, 29 (2), 41–76.

Tow, W. N. F. H., Dell, P., & Venable, J. (2010). Understanding information disclosure behaviour in Australian Facebook users. Journal of Information Technology, 25 (2), 126–136.

Trier, M., & Richter, A. (2015). The deep structure of organizational online networking–an actor-oriented case study. Information Systems Journal, 25 (5), 465–488.

Turel, O. (2015). Quitting the use of a habituated hedonic information system: A theoretical model and empirical examination of Facebook users. European Journal of Information Systems, 24 (4), 431–446.

Turel, O., & Serenko, A. (2012). The benefits and dangers of enjoyment with social networking websites. European Journal of Information Systems, 21 (5), 512–528.

Van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer, ISSI . Newsletter, 7 (3), 50–54.

Van Osch, W., & Steinfield, C. W. (2016). Team boundary spanning: Strategic implications for the implementation and use of enterprise social media. Journal of Information Technology, 31 (2), 207–225.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478.

Vishwanath, A. (2015). Diffusion of deception in social media: Social contagion effects and its antecedents. Information Systems Frontiers, 17 (6), 1353–1367.

Wakefield, R., & Wakefield, K. (2016). Social media network behavior: A study of user passion and affect. The Journal of Strategic Information Systems, 25 (2), 140–156.

Wang, Y., Zeng, D., Zhu, B., Zheng, X., & Wang, F. (2014). Patterns of news dissemination through online news media: A case study in China. Information Systems Frontiers, 16 (4), 557–570.

Wattal, S., Schuff, D., Mandviwalla, M., & Williams, C. B. (2010). Web 2.0 and politics: The 2008 US presidential election and an e-politics research agenda. MIS Quarterly, 34 (4), 669–688.

Wei, C., Khoury, R., & Fong, S. (2013). Web 2.0 recommendation service by multi-collaborative filtering trust network algorithm. Information Systems Frontiers, 15 (4), 533–551.

White, H. D., & Griffith, B. C. (1981). Author Cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32 (3), 163–171.

Wu, L. (2013). Social network effects on productivity and job security: Evidence from the adoption of a social networking tool. Information Systems Research, 24 (1), 30–51.

Wu, J., & Gaytán, E. A. A. (2013). The role of online seller reviews and product price on buyers' willingness-to-pay: A risk perspective. European Journal of Information Systems, 22 (4), 416–433.

Xie, K., & Lee, Y. J. (2015). Social media and brand purchase: Quantifying the effects of exposures to earned and owned social media activities in a two-stage decision making model. Journal of Management Information Systems, 32 (2), 204–238.

Xu, S. X., & Zhang, X. M. (2013). Impact of Wikipedia on market information environment: Evidence on management disclosure and investor reaction. MIS Quarterly, 37 (4), 1043–1068.

Xu, Y. C., Yang, Y., Cheng, Z., & Lim, J. (2014). Retaining and attracting users in social networking services: An empirical investigation of cyber migration. The Journal of Strategic Information Systems, 23 (3), 239–253.

Xu, B., Xu, Z., & Li, D. (2016). Internet aggression in online communities: A contemporary deterrence perspective. Information Systems Journal, 26 (6), 641–667.

Yan, L., & Tan, Y. (2014). Feeling blue? Go online: An empirical study of social support among patients. Information Systems Research, 25 (4), 690–709.

Yan, L., Peng, J., & Tan, Y. (2015a). Network dynamics: How can we find patients like us? Information Systems Research, 26 (3), 496–512.

Yan, X., Wang, J., & Chau, M. (2015b). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17 (3), 645–657.

Yu, J., Hu, P. J. H., & Cheng, T. H. (2015). Role of affect in self-disclosure on social network websites: A test of two competing models. Journal of Management Information Systems, 32 (2), 239–277.

Zeng, X., & Wei, L. (2013). Social ties and user content generation: Evidence from Flickr. Information Systems Research, 24 (1), 71–87.

Zhang, J., & Piramuthu, S. (2016). Product recommendation with latent review topics. Information Systems Frontiers , 1–9. https://doi.org/10.1007/s10796-016-9697-z .

Zhang, X., & Wang, C. (2012). Network positions and contributions to online public goods: The case of Chinese Wikipedia. Journal of Management Information Systems, 29 (2), 11–40.

Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. Y. (2016). What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33 (2), 456–481.

Zhao, L., Detlor, B., & Connelly, C. E. (2016). Sharing knowledge in Social Q&a Sites: The unintended consequences of extrinsic motivation. Journal of Management Information Systems, 33 (1), 70–100.

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Kuttimani Tamilmani, Nripendra P. Rana, Pushp Patil & Yogesh K. Dwivedi

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Likes, Shares, and Beyond: Exploring the Impact of Social Media in Essays

Table of contents

• 1 Definition and Explanation of a Social Media Essay
• 2.1 Topics for an Essay on Social Media and Mental Health
• 2.2 Social Dynamics
• 2.3 Social Media Essay Topics about Business
• 2.4 Politics
• 3 Research and Analysis
• 4 Structure Social Media Essay
• 5 Tips for Writing Essays on Social Media
• 6 Examples of Social Media Essays
• 7 Navigating the Social Media Labyrinth: Key Insights

In the world of digital discourse, our article stands as a beacon for those embarking on the intellectual journey of writing about social media. It is a comprehensive guide for anyone venturing into the dynamic world of social media essays. Offering various topics about social media and practical advice on selecting engaging subjects, the piece delves into research methodologies, emphasizing the importance of credible sources and trend analysis. Furthermore, it provides invaluable tips on structuring essays, including crafting compelling thesis statements and hooks balancing factual information with personal insights. Concluding with examples of exemplary essays, this article is an essential tool for students and researchers alike, aiding in navigating the intricate landscape of its impact on society.

Definition and Explanation of a Social Media Essay

Essentially, when one asks “What is a social media essay?” they are referring to an essay that analyzes, critiques, or discusses its various dimensions and effects. These essays can range from the psychological implications of its use to its influence on politics, business strategies, and social dynamics.

A social media essay is an academic or informational piece that explores various aspects of social networking platforms and their impact on individuals and society.

In crafting such an essay, writers blend personal experiences, analytical perspectives, and empirical data to paint a full picture of social media’s role. For instance, a social media essay example could examine how these platforms mold public opinion, revolutionize digital marketing strategies, or raise questions about data privacy ethics. Through a mix of thorough research, critical analysis, and personal reflections, these essays provide a layered understanding of one of today’s most pivotal digital phenomena.

Great Social Media Essay Topics

When it comes to selecting a topic for your essay, consider its current relevance, societal impact, and personal interest. Whether exploring the effects on business, politics, mental health, or social dynamics, these social media essay titles offer a range of fascinating social media topic ideas. Each title encourages an exploration of the intricate relationship between social media and our daily lives. A well-chosen topic should enable you to investigate the impact of social media, debate ethical dilemmas, and offer unique insights. Striking the right balance in scope, these topics should align with the objectives of your essays, ensuring an informative and captivating read.

Topics for an Essay on Social Media and Mental Health

• The Impact of Social Media on Self-Esteem.
• Unpacking Social Media Addiction: Causes, Effects, and Solutions.
• Analyzing Social Media’s Role as a Catalyst for Teen Depression and Anxiety.
• Social Media and Mental Health Awareness: A Force for Good?
• The Psychological Impacts of Cyberbullying in the Social Media Age.
• The Effects of Social Media on Sleep and Mental Health.
• Strategies for Positive Mental Health in the Era of Social Media.
• Real-Life vs. Social Media Interactions: An Essay on Mental Health Aspects.
• The Mental Well-Being Benefits of a Social Media Detox.
• Social Comparison Psychology in the Realm of Social Media.

Social Dynamics

• Social Media and its Impact on Interpersonal Communication Skills: A Cause and Effect Essay on Social Media.
• Cultural Integration through Social Media: A New Frontier.
• Interpersonal Communication in the Social Media Era: Evolving Skills and Challenges.
• Community Building and Social Activism: The Role of Social Media.
• Youth Culture and Behavior: The Influence of Social Media.
• Privacy and Personal Boundaries: Navigating Social Media Challenges.
• Language Evolution in Social Media: A Dynamic Shift.
• Leveraging Social Media for Social Change and Awareness.
• Family Dynamics in the Social Media Landscape.
• Friendship in the Age of Social Media: An Evolving Concept.

Social Media Essay Topics about Business

• Influencer Marketing on Social Media: Impact and Ethics.
• Brand Building and Customer Engagement: The Power of Social Media.
• The Ethics and Impact of Influencer Marketing in Social Media.
• Measuring Business Success Through Social Media Analytics.
• The Changing Face of Advertising in the Social Media World.
• Revolutionizing Customer Service in the Social Media Era.
• Market Research and Consumer Insights: The Social Media Advantage.
• Small Businesses and Startups: The Impact of Social Media.
• Ethical Dimensions of Social Media Advertising.
• Consumer Behavior and Social Media: An Intricate Relationship.
• The Role of Social Media in Government Transparency and Accountability
• Social Media’s Impact on Political Discourse and Public Opinion.
• Combating Fake News on Social Media: Implications for Democracy.
• Political Mobilization and Activism: The Power of Social Media.
• Social Media: A New Arena for Political Debates and Discussions.
• Government Transparency and Accountability in the Social Media Age.
• Voter Behavior and Election Outcomes: The Social Media Effect.
• Political Polarization: A Social Media Perspective.
• Tackling Political Misinformation on Social Media Platforms.
• The Ethics of Political Advertising in the Social Media Landscape.
• Memes as a Marketing Tool: Successes, Failures, and Pros of Social Media.
• Shaping Public Opinion with Memes: A Social Media Phenomenon.
• Political Satire and Social Commentary through Memes.
• The Psychology Behind Memes: Understanding Their Viral Nature.
• The Influence of Memes on Language and Communication.
• Tracing the History and Evolution of Internet Memes.
• Memes in Online Communities: Culture and Subculture Formation.
• Navigating Copyright and Legal Issues in the World of Memes.
• Memes as a Marketing Strategy: Analyzing Successes and Failures.
• Memes and Global Cultural Exchange: A Social Media Perspective.

Research and Analysis

In today’s fast-paced information era, the ability to sift through vast amounts of data and pinpoint reliable information is more crucial than ever. Research and analysis in the digital age hinge on identifying credible sources and understanding the dynamic landscape. Initiating your research with reputable websites is key. Academic journals, government publications, and established news outlets are gold standards for reliable information. Online databases and libraries provide a wealth of peer-reviewed articles and books. For websites, prioritize those with domains like .edu, .gov, or .org, but always critically assess the content for bias and accuracy. Turning to social media, it’s a trove of real-time data and trends but requires a discerning approach. Focus on verified accounts and official pages of recognized entities.

Analyzing current trends and user behavior is crucial for staying relevant. Platforms like Google Trends, Twitter Analytics, and Facebook Insights offer insights into what’s resonating with audiences. These tools help identify trending topics, hashtags, and the type of content that engages users. Remember, it reflects and influences public opinion and behavior. Observing user interactions, comments, and shares can provide a deeper understanding of consumer attitudes and preferences. This analysis is invaluable for tailoring content, developing marketing strategies, and staying ahead in a rapidly evolving digital landscape.

Structure Social Media Essay

In constructing a well-rounded structure for a social media essay, it’s crucial to begin with a strong thesis statement. This sets the foundation for essays about social media and guides the narrative.

Thesis Statements

A thesis statement is the backbone of your essay, outlining the main argument or position you will explore throughout the text. It guides the narrative, providing a clear direction for your essay and helping readers understand the focus of your analysis or argumentation. Here are some thesis statements:

• “Social media has reshaped communication, fostering a connected world through instant information sharing, yet it has come at the cost of privacy and genuine social interaction.”
• “While social media platforms act as potent instruments for societal and political transformation, they present significant challenges to mental health and the authenticity of information.”
• “The role of social media in contemporary business transcends mere marketing; it impacts customer relationships, shapes brand perception, and influences operational strategies.”

Social Media Essay Hooks

Social media essay hooks are pivotal in grabbing the reader’s attention right from the beginning and compelling them to continue reading. A well-crafted hook acts as the engaging entry point to your essay, setting the tone and framing the context for the discussion that will follow.

Here are some effective social media essay hooks:

• “In a world where a day without social media is unimaginable, its pervasive presence is both a testament to its utility and a source of various societal issues.”
• “Each scroll, like, and share on social media platforms carries the weight of influencing public opinion and shaping global conversations.”
• “Social media has become so ingrained in our daily lives that its absence would render the modern world unrecognizable.”

Introduction:

Navigating the digital landscape, an introduction for a social media essay serves as a map, charting the terrain of these platforms’ broad influence across various life aspects. This section should briefly summarize the scope of the essay, outlining both the benefits and the drawbacks, and segue into the thesis statement.

When we move to the body part of the essay, it offers an opportunity for an in-depth exploration and discussion. It can be structured first to examine the positive aspects of social media, including improved communication channels, innovative marketing strategies, and the facilitation of social movements. Following this, the essay should address the negative implications, such as issues surrounding privacy, the impact on mental health, and the proliferation of misinformation. Incorporating real-world examples, statistical evidence, and expert opinions throughout the essay will provide substantial support for the arguments presented.

Conclusion:

It is the summit of the essay’s exploration, offering a moment to look back on the terrain covered. The conclusion should restate the thesis in light of the discussions presented in the body. It should summarize the key points made, reflecting on the multifaceted influence of social media in contemporary society. The essay should end with a thought-provoking statement or question about the future role of social media, tying back to the initial hooks and ensuring a comprehensive and engaging end to the discourse.

Tips for Writing Essays on Social Media

In the ever-evolving realm of digital dialogue, mastering the art of essay writing on social media is akin to navigating a complex web of virtual interactions and influences. Writing an essay on social media requires a blend of analytical insight, factual accuracy, and a nuanced understanding of the digital landscape. Here are some tips to craft a compelling essay:

• Incorporate Statistical Data and Case Studies

Integrate statistical data and relevant case studies to lend credibility to your arguments. For instance, usage statistics, growth trends, and demographic information can provide a solid foundation for your points. Case studies, especially those highlighting its impact on businesses, politics, or societal change, offer concrete examples that illustrate your arguments. Ensure your sources are current and reputable to maintain the essay’s integrity.

• Balance Personal Insights with Factual Information

While personal insights can add a unique perspective to your essay, balancing them with factual information is crucial. Personal observations and experiences can make your essay relatable and engaging, but grounding these insights in factual data ensures credibility and helps avoid bias.

• Respect Privacy

When discussing real-world examples or case studies, especially those involving individuals or specific organizations, be mindful of privacy concerns. Avoid sharing sensitive information, and always respect the confidentiality of your sources.

• Maintain an Objective Tone

It is a polarizing topic, but maintaining an objective tone in your essay is essential. Avoid emotional language and ensure that your arguments are supported by evidence. An objective approach allows readers to form opinions based on the information presented.

• Use Jargon Wisely

While using social media-specific terminology can make your essay relevant and informed, it’s important to use jargon judiciously. Avoid overuse and ensure that terms are clearly defined for readers who might not be familiar with their lingo.

Examples of Social Media Essays

Title: The Dichotomy of Social Media: A Tool for Connection and a Platform for Division

Introduction

In the digital era, social media has emerged as a paradoxical entity. It serves as a bridge connecting distant corners of the world and a battleground for conflicting ideologies. This essay explores this dichotomy, utilizing statistical data, case studies, and real-world examples to understand its multifaceted impact on society.

Section 1 – Connection Through Social Media:

Social media’s primary allure lies in its ability to connect. A report by the Pew Research Center shows that 72% of American adults use some form of social media, where interactions transcend geographical and cultural barriers. This statistic highlights the platform’s popularity and role in fostering global connections. An exemplary case study of this is the #MeToo movement. Originating as a hashtag on Twitter, it grew into a global campaign against sexual harassment, demonstrating its power to mobilize and unify people for a cause.

However, personal insights suggest that while it bridges distances, it can also create a sense of isolation. Users often report feeling disconnected from their immediate surroundings, hinting at the platform’s double-edged nature. Despite enabling connections on a global scale, social media can paradoxically alienate individuals from their local context.

Section 2 – The Platform for Division

Conversely, social media can amplify societal divisions. Its algorithm-driven content can create echo chambers, reinforcing users’ preexisting beliefs. A study by the Knight Foundation found that it tends to polarize users, especially in political contexts, leading to increased division. This is further exacerbated by the spread of misinformation, as seen in the 2016 U.S. Presidential Election case, where it was used to disseminate false information, influencing public opinion and deepening societal divides.

Respecting privacy and maintaining an objective tone, it is crucial to acknowledge that social media is not divisive. Its influence is determined by both its usage and content. Thus, it is the obligation of both platforms to govern content and consumers to access information.

In conclusion, it is a complex tool. It has the unparalleled ability to connect individuals worldwide while possessing the power to divide. Balancing the personal insights with factual information presented, it’s clear that its influence is a reflection of how society chooses to wield it. As digital citizens, it is imperative to use it judiciously, understanding its potential to unite and divide.

Delving into the intricacies of social media’s impact necessitates not just a keen eye for detail but an analytical mindset to dissect its multifaceted layers. Analysis is paramount because it allows us to navigate through the vast sea of information, distinguishing between mere opinion and well-supported argumentation.

This essay utilizes tips for writing a social media essay. Statistical data from the Pew Research Center and the Knight Foundation lend credibility to the arguments. The use of the #MeToo movement as a case study illustrates its positive impact, while the reference to the 2016 U.S. Presidential Election demonstrates its negative aspects. The essay balances personal insights with factual information, respects privacy, maintains an objective tone, and appropriately uses jargon. The structure is clear and logical, with distinct sections for each aspect of its impact, making it an informative and well-rounded analysis of its role in modern society.

Navigating the Social Media Labyrinth: Key Insights

In the digital age, the impact of social media on various aspects of human life has become a critical area of study. This article has provided a comprehensive guide for crafting insightful and impactful essays on this subject, blending personal experiences with analytical rigor. Through a detailed examination of topics ranging from mental health and social dynamics to business and politics, it has underscored the dual nature of social media as both a unifying and divisive force. The inclusion of statistical data and case studies has enriched the discussion, offering a grounded perspective on the nuanced effects of these platforms.

The tips and structures outlined serve as a valuable framework for writers to navigate the complex interplay between social media and societal shifts. As we conclude, it’s clear that understanding social media’s role requires a delicate balance of critical analysis and open-mindedness. Reflecting on its influence, this article guides the creation of thoughtful essays and encourages readers to ponder the future of digital interactions and their implications for the fabric of society.

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• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

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Introduction.

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

Best, P., Manktelow, R. & Taylor, B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child Youth Serv. Rev. 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 (2014).

Article   Google Scholar  

James, C. et al. Digital life and youth well-being, social connectedness, empathy, and narcissism. Pediatrics 140 , S71–S75. https://doi.org/10.1542/peds.2016-1758F (2017).

Article   PubMed   Google Scholar  

McCrae, N., Gettings, S. & Purssell, E. Social media and depressive symptoms in childhood and adolescence: A systematic review. Adolesc. Res. Rev. 2 , 315–330. https://doi.org/10.1007/s40894-017-0053-4 (2017).

Sarmiento, I. G. et al. How does social media use relate to adolescents’ internalizing symptoms? Conclusions from a systematic narrative review. Adolesc Res Rev , 1–24, doi:10.1007/s40894-018-0095-2 (2018).

Orben, A. Teenagers, screens and social media: A narrative review of reviews and key studies. Soc. Psychiatry Psychiatr. Epidemiol. https://doi.org/10.1007/s00127-019-01825-4 (2020).

Orben, A., Dienlin, T. & Przybylski, A. K. Social media’s enduring effect on adolescent life satisfaction. Proc. Natl. Acad. Sci. USA 116 , 10226–10228. https://doi.org/10.1073/pnas.1902058116 (2019).

Article   CAS   PubMed   Google Scholar  

Whitlock, J. & Masur, P. K. Disentangling the association of screen time with developmental outcomes and well-being: Problems, challenges, and opportunities. JAMA https://doi.org/10.1001/jamapediatrics.2019.3191 (2019).

Hamaker, E. L. In Handbook of Research Methods for Studying Daily Life (eds Mehl, M. R. & Conner, T. S.) 43–61 (Guilford Press, New York, 2012).

Schmiedek, F. & Dirk, J. In The Encyclopedia of Adulthood and Aging (ed. Krauss Whitbourne, S.) 1–6 (Wiley, 2015).

Keijsers, L. & van Roekel, E. In Reframing Adolescent Research (eds Hendry, L. B. & Kloep, M.) (Routledge, 2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Does time spent using social media impact mental health? An eight year longitudinal study. Comput. Hum. Behav. 104 , 106160. https://doi.org/10.1016/j.chb.2019.106160 (2020).

Boers, E., Afzali, M. H., Newton, N. & Conrod, P. Association of screen time and depression in adolescence. JAMA 173 , 853–859. https://doi.org/10.1001/jamapediatrics.2019.1759 (2019).

Jensen, M., George, M. J., Russell, M. R. & Odgers, C. L. Young adolescents’ digital technology use and mental health symptoms: Little evidence of longitudinal or daily linkages. Clin. Psychol. Sci. https://doi.org/10.1177/2167702619859336 (2019).

Valkenburg, P. M. The limited informativeness of meta-analyses of media effects. Perspect. Psychol. Sci. 10 , 680–682. https://doi.org/10.1177/1745691615592237 (2015).

Pearce, L. J. & Field, A. P. The impact of “scary” TV and film on children’s internalizing emotions: A meta-analysis. Hum. Commun.. Res. 42 , 98–121. https://doi.org/10.1111/hcre.12069 (2016).

Howard, M. C. & Hoffman, M. E. Variable-centered, person-centered, and person-specific approaches. Organ. Res. Methods 21 , 846–876. https://doi.org/10.1177/1094428117744021 (2017).

Valkenburg, P. M. & Peter, J. The differential susceptibility to media effects model. J. Commun. 63 , 221–243. https://doi.org/10.1111/jcom.12024 (2013).

Eid, M. & Diener, E. Global judgments of subjective well-being: Situational variability and long-term stability. Soc. Indic. Res. 65 , 245–277. https://doi.org/10.1023/B:SOCI.0000003801.89195.bc (2004).

Kross, E. et al. Facebook use predicts declines in subjective well-being in young adults. PLoS ONE 8 , e69841. https://doi.org/10.1371/journal.pone.0069841 (2013).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Reissmann, A., Hauser, J., Stollberg, E., Kaunzinger, I. & Lange, K. W. The role of loneliness in emerging adults’ everyday use of facebook—An experience sampling approach. Comput. Hum. Behav. 88 , 47–60. https://doi.org/10.1016/j.chb.2018.06.011 (2018).

Rutledge, R. B., Skandali, N., Dayan, P. & Dolan, R. J. A computational and neural model of momentary subjective well-being. Proc. Natl. Acad. Sci. USA 111 , 12252–12257. https://doi.org/10.1073/pnas.1407535111 (2014).

Article   ADS   CAS   PubMed   Google Scholar  

Tov, W. In Handbook of Well-being (eds Diener, E.D. et al. ) (DEF Publishers, 2018).

Harter, S. The Construction of the Self: Developmental and Sociocultural Foundations (Guilford Press, New York, 2012).

Steinberg, L. Adolescence . Vol. 9 (McGraw-Hill, 2011).

Rideout, V. & Fox, S. Digital Health Practices, Social Media Use, and Mental Well-being Among Teens and Young Adults in the US (HopeLab, San Francisco, 2018).

Google Scholar  

Waterloo, S. F., Baumgartner, S. E., Peter, J. & Valkenburg, P. M. Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media Soc. 20 , 1813–1831. https://doi.org/10.1177/1461444817707349 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Rideout, V. & Robb, M. B. Social Media, Social Life: Teens Reveal their Experiences (Common Sense Media, San Fransico, 2018).

van Driel, I. I., Pouwels, J. L., Beyens, I., Keijsers, L. & Valkenburg, P. M. 'Posting, Scrolling, Chatting & Snapping': Youth (14–15) and Social Media in 2019 (Center for Research on Children, Adolescents, and the Media (CcaM), Universiteit van Amsterdam, 2019).

Verduyn, P. et al. Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J. Exp. Psychol. 144 , 480–488. https://doi.org/10.1037/xge0000057 (2015).

Valkenburg, P. M. & Peter, J. Five challenges for the future of media-effects research. Int. J. Commun. 7 , 197–215 (2013).

Verduyn, P., Ybarra, O., Résibois, M., Jonides, J. & Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issues Policy Rev. 11 , 274–302. https://doi.org/10.1111/sipr.12033 (2017).

Radovic, A., Gmelin, T., Stein, B. D. & Miller, E. Depressed adolescents’ positive and negative use of social media. J. Adolesc. 55 , 5–15. https://doi.org/10.1016/j.adolescence.2016.12.002 (2017).

Valkenburg, P. M., Peter, J. & Schouten, A. P. Friend networking sites and their relationship to adolescents’ well-being and social self-esteem. Cyberpsychol. Behav. 9 , 584–590. https://doi.org/10.1089/cpb.2006.9.584 (2006).

van Roekel, E., Keijsers, L. & Chung, J. M. A review of current ambulatory assessment studies in adolescent samples and practical recommendations. J. Res. Adolesc. 29 , 560–577. https://doi.org/10.1111/jora.12471 (2019).

van Roekel, E., Scholte, R. H. J., Engels, R. C. M. E., Goossens, L. & Verhagen, M. Loneliness in the daily lives of adolescents: An experience sampling study examining the effects of social contexts. J. Early Adolesc. 35 , 905–930. https://doi.org/10.1177/0272431614547049 (2015).

Neumann, A., van Lier, P. A. C., Frijns, T., Meeus, W. & Koot, H. M. Emotional dynamics in the development of early adolescent psychopathology: A one-year longitudinal Study. J. Abnorm. Child Psychol. 39 , 657–669. https://doi.org/10.1007/s10802-011-9509-3 (2011).

Hox, J., Moerbeek, M. & van de Schoot, R. Multilevel Analysis: Techniques and Applications 3rd edn. (Routledge, London, 2018).

Wang, L. P. & Maxwell, S. E. On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychol. Methods 20 , 63–83. https://doi.org/10.1037/met0000030 (2015).

Satorra, A. & Bentler, P. M. Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika 75 , 243–248. https://doi.org/10.1007/s11336-009-9135-y (2010).

Article   MathSciNet   PubMed   PubMed Central   MATH   Google Scholar  

Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6 , 461–464. https://doi.org/10.1214/aos/1176344136 (1978).

Article   MathSciNet   MATH   Google Scholar  

Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19 , 716–723. https://doi.org/10.1109/TAC.1974.1100705 (1974).

Article   ADS   MathSciNet   MATH   Google Scholar  

Keijsers, L. et al. What drives developmental change in adolescent disclosure and maternal knowledge? Heterogeneity in within-family processes. Dev. Psychol. 52 , 2057–2070. https://doi.org/10.1037/dev0000220 (2016).

R Core Team R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2017).

Muthén, L. K. & Muthén, B. O. Mplus User’s Guide 8th edn. (Muthén & Muthén, Los Angeles, 2017).

Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L. & Valkenburg, P. M. Dataset belonging to Beyens et al. (2020). The effect of social media on well-being differs from adolescent to adolescent. https://doi.org/10.21942/uva.12497990 (2020).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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Chapter 6: 21st-century media and issues

6.10.2 Social media and communication (research essay)

Lindsey Matier

English 102, April 2021

Communication is extremely important in today’s world, whether it be verbal or nonverbal. It can take place through many different forms such as through writing, speaking, listening and physical actions. These forms of communication evolve and continue to improve over time. As humans, we rely on communication for almost everything and it is a way of life. Communication has evolved from talking to writing letters to texting or talking over the phone. Every time a new form of communication is brought up and becomes more popular, we have to adapt and evolve to that new lifestyle. Throughout all the new forms of communication and ways of evolving, social media has been one of the most influential so far. Social media has allowed us to create new ways of communicating, such as texting or posting through different apps. It can connect us with people all over the world and give us a platform to express ourselves in ways that have not been possible before. While social media started off as a small form of technology, it has morphed into aspects of our everyday life. Now there are apps for everything from social media profiles to online shopping. While social media and technology itself has evolved, this has also affected our communication with each other and the world. Social media has created a fast track for information in a matter of seconds. It can give people a platform with millions of followers overnight for doing practically anything. It can help people express themselves in new ways and connect with people who have similar interests. The end goal of social media is to make people happy and ultimately make lives easier.

Introduction

With all this being said, it is evident that social media is in our everyday lives and will continue to change. It has a very strong grip on society as social media usage continues to rise throughout the years. Generalizing social media, we are exposed to forms of media at almost all times of the day. Answering the question of what media is will help give a better understanding of social media as a whole. Media can be defined as a way of mass communication. This could include siting in the car listening to ads on the radio all the way to scrolling on twitter. We are exposed to social media less often than generalized media, but it tends to come in greater quantities when exposed. For example, for people that wake up and check twitter it is an instant flood of information with every scroll. Everything from politics to sports to celebrity news is available at the fingertips. The concern is not all focused on the overwhelming information, but also the overwhelming number of comments and opinions. If we wanted to debate or talk about something before social media it had to be done in person, face to face. Now with social media, we are able to fight with people in comment sections on a backup account with a different name and no connection to who we really are. This new form of communication takes away the vulnerability of speaking to people and having genuine conversation, and makes up for it in internet trolls. Overall, social media is impacting the way we communicate with each other and the real questions are: Is social media impacting us in a positive or negative way? Do the positive aspects outweigh the negative aspects? Is social media hindering the way we communicate in person with each other? Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How is social media impacting younger generation’s communication versus older generation’s communication? How can we help improve our communication skills on social media and in real life?

Personal Research 

Along with the other studies that I found from the sources I chose, I also conducted my own study to determine more accurate and recent data. I asked students mostly within high school and college range questions relating to social media and communication. I tried to get a wide range of data dealing with social media apps, screen time, and overall communication as a result of social media. I expected to see almost all negative responses about social media and communication. I figured that most people would respond saying that it has affected them negatively rather than positively, but the results were different compared to what I expected.

The first questions I asked had to do with social media itself. I asked questions about their most used social media apps, screen time, what age they were allowed to start using social media, and whether or not they think social media has had a negative or positive impact on them. As expected, most of the social media apps were some of the most popular ones like Snapchat, Instagram, and TikTok. Overall, the average screen time for all apps was evenly split between 4-6 and 6-8 hours, which I also expected. Something that did surprise me was the amount of time spent on certain social media apps. The data was split pretty evenly three ways and all between 1-4 hours. The next two questions dealt with when they group surveyed started using social media. I asked these questions because a lot of the points I want to discuss later in my paper have to deal with age and whether younger generations are suffering when it comes to communication. More than half the people surveyed said that they wished that they had waited to get social media until they were older. Some said that it is not appropriate for younger kids and that it is just toxic in general. Something that I really like that a couple people mentioned was that in reality, social media at a young age is stupid and useless. A lot of people said they wish they would have enjoyed their childhood more and they would be more extroverted now if they had not been exposed that early. The last question of this section that I asked was if they thought social media has had a more positive or negative impact on them. Overall, the data was split but leaning slightly towards the more positive side. The positive answers mostly dealt with being able to talk to stay in contact with people and meeting new friends. The negative answers all related to mental health and feeling bad about themselves. A lot of people said it is toxic and very controlling and takes up too much of our time.

The next set of questions I asked had to do more with communication and interaction with and without social media. I asked questions like how they feel about social media and how it has impacted their communication, their mental health, and if it has made our lives easier. I decided to ask questions like these because I figured I would get a wide range of responses and a lot of people’s different opinions. I started off by asking if people are an introvert or an extrovert to get an idea of what the responses would be like, and 66% said somewhere in between the two. The response for the next question really shocked me because I received such a one-side response. I asked if they think social media has impacted their communication and the way they interact with others and 75% (18/24 people) said yes. This is the information that I was looking for along with the next two questions. The next question asked if they think social media has negatively impacted their mental health and 50% said yes. I also plan on using this as a research question to show that social media can affect our mental health and therefore affect the way we interact with and around other people. The last two questions are similar but the responses were both very good. Almost everyone answered yes to the question asking if social media has made our lives easier. Everyone that answered yes said they think so because it helps them talk to friends, stay in touch with people they do not see as much, and meet new people that they are comfortable talking to. The people that said no also made good points such as it takes over our lives and it is filled with too much hate and cancel culture. I agree with both sides and am very happy that people can feel a positive response especially when it comes to communicating with other people online. The last question I asked was used to wrap up the whole survey and topic. I asked if they think social media has made our generation’s communication improve or worsen. The data was pretty evenly split, and most people gave a positive and a negative. The people that said improve gave that answer because they said it broadens our communication and allows us to talk to people at a wider range. The people who said it has made it worse all said that it is ruining our face-to-face interaction and causing us to lose emotion. They said that some people do not even know how to have a proper in person conversation and that they are too dependent on their phones. Overall, I agree with both arguments that people made but I do think that the positives outweigh the negatives in most of these situations and questions.

Research Questions

The first question I want to ask has to deal with the overall social media and communication connection and has multiple other questions I would like to cover within it. The main question is: Is social media hindering the way we communicate with each other? I also want to touch on questions like: Is social media impacting us in a positive or negative way? Do the positives outweigh the negatives? The second set of research questions I have is: Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How can we help improve our communication skills on social media and in real life? How is social media impacting younger generation’s communication versus older generation’s communication?

Research Question One

Social media and communication have a direct connection to each other and both have a strong impact on the outcome of the other. My first research question has to do with that. My questions center around how social media has impacted our communication, and whether or not it is positive or negative. First, I think it is important to note the changes and different characteristics that come into play when talking about this. Things like age and problems going on in our world can affect our social media usage and communication. While we connect to people on a deeper level when talking to the in person, social media has also given us a newer and more broad way of communicating. The article “How Social Media Affects Our Ability to Communicate” by Stacey Hanke, talks about different ways social media has impacted our communication. Social media has become so relevant in our day to day lives and Hanke describes it in a couple different ways. She describes it as information binging and the fear of missing out, social graces and conversational boredom. Within these, she explains how social media has become an excuse and escape to talk to people face to face. Hanke also talks about how even though it is limiting our in person communication, it can sometimes make communicating in general easier, by being able to talk to each other in just a few words (Hanke 1). In another article by Ryan J. Fuller titled “The Impact of Social Media Use on Our Social Skills”, he discusses similar topics to Hanke’s article but also brings up more positive attributes of social media. Fuller starts of his article by giving some statistics, stating that 75% of teens own cellphones and 25% of them using it for social media, and also says that they use 7.5 hours a day using it (Fuller 1). I am glad that this was brought up because it is important to know how much time is spent on social media, scrolling through feed. Next, Fuller starts to discuss some of the benefits of social media. He briefly explains how social media is beneficial because we are able to stay in touch with our friends and family, and share important parts of our lives with them. He also explains how it helps people reach out to new friends and provide themselves with more opportunities (Fuller 1). Overall, I really like that he mentioned these because it is important to keep in mind the vast majority of social media and communication. While some use it for more simpler purposes likes just keeping up to date with what is going on in the world, others use it to make new friends, find new job opportunities, and stay in touch with people. Another topic I find important when it comes to answering this research question is how Covid affected everything. With the pandemic, we were left inside with nothing to do but what was at our fingertips. This pandemic increased social media usage drastically. The article “Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data” by Danny Valdez et al, shows extensive research into determining just how much social media usage in the United States increased during the pandemic. They did experiments and surveys to determine multiple responses to research questions and show how much we rely on social media to communicate with each other. During the pandemic, everyone spent more time on their social media and their phone in general, probably more than they would like to admit. The article helps give more insight into this claim. There is the idea that social media was meant as an addition to our lives. For some people, it has become an addiction and a new piece of their life. The article focuses on how social media could be a toxic place and have a negative effect on our mental health. The time period for this information focuses around the COVID-19 pandemic. Using data from Twitter, Valdez created a study to determine the mood of people during the pandemic and the usage throughout (Valdez et al 2). Collecting tweets with certain hashtags and during time periods, the goal was to determine how much the pandemic affected people’s moods, and how much they put out and shared on social media. They used hashtags, timeline data, and tweets from different periods such as the first lockdown, different stay at home orders, etc. Given the responses to the data, they were able to determine the increase in social media usage. We cannot determine if this had a positive or negative effect on the people who were using Twitter, but we can infer that social media is becoming a key part of our lives. Not being able to talk to people as much in person during the first few months of the pandemic greatly affected communication, in positive and negative ways. Communication over the phone increased due to the amount of free time that people had and were able to spend talking to others. Contrary to that, in person communication also decreased given that people were not really allowed to leave the house. The next article by Tayebi et al, “The Role of Information Systems in Communication Through Social Media” focuses a lot about how we have evolved over time with social media and communication. They start off by talking about how social networks are like social media societies. They explain it by resembling it to a human society, as it is filled with people communicating, regardless of time or place. They also exemplify other aspects such as emotional support, information, emotions (Tayebi 2). Social media is constantly looked at through such a negative light due to some of the major bad events that have taken place. While it can be difficult at times to look past the negatives, it is important to recognize and acknowledge the positives. The growth of scientific research would not be possible without the amount of information received from the media (Tayebi 3). Without social media and media in general, we would not be where we are today as a society. As mentioned earlier, it is so easy to get lost in the negative aspects of social media and discard the positive ones. Positive parts of social media such as widespread communication and unlimited access to information makes it all worth it. Staying on topic with positive aspects of social media and communication, social media in the workplace has also broken down barriers for communication. The article “A Guide to the Successful Use of Social Media in the Workplace” by Clark Boyd gives insight into how social media has improved the workplace, and ultimately communication and interaction as a whole. Companies can use social media as a form of branding and way to communicate their products (Boyd 4). Boyd states, “Harvard Business Review finds that 82% of employees believe social media improves work relationships. Left to their own devices, your teams will connect and communicate on social networks, both inside and outside the office.” This directly relates to the research question asking whether social media hinders our communication with each other. Social media also helps when it comes to dealing with complaints placed online. By seeing these through social media, it can help the company communicate either with the person or their company the concerns that are being stated (Boyd 9). Overall, it is safe to say that social media has directly affected communication throughout different aspects of our lives.

Research Question Two

My second set of research questions has a lot to do with the future and how we can improve. Questions such as: Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How can we help improve our communication skills on social media and in real life? How is social media impacting younger generation’s communication versus older generation’s communication? The article “What is Literacy” by James Paul Gee talks a lot about the basics of communication. I find this an important article to talk about before I go into more detail with this second research question. Gee explains discourse as a socially accepted way of speaking, thinking, and acting (Gee 1). It is important to note this because social media has changed that discourse for us. We no longer communicate and interact the same way in which we use to therefore almost giving us a new discourse. Another thing Gee discusses is identity kits. Gee explains identity kits as “appropriate costumes and instructions on how to act and talk” (Gee 2). This relates to social media because there is a certain way we communicate online that we wouldn’t do in person. For example, we use emojis and abbreviations to communicate on social media or over text, but this is something we would not do when communicating face-to-face. There are also some basic well-known rules of social media that follow along the lines of an identity kit. Such as, for Instagram it is a common idea not to like people’s pictures from too long ago. When you say this aloud it sounds like it is not a big deal and silly almost, but for people that use social media it is something that makes sense. The next article is going to focus more on the question that has to do with room for improvement of communication. The article “The Positive Effect of Not Following Others on Social Media” by Francesca Valsesia, Davide Proserpio, and Joseph C. Nunes involves how we deal with social media and how we react to it. The article has a lot to do with pyramid schemes and marketing schemes on social media, simply due to follower count. Social media has a lot of power over us and the content we see. Influencers have too much impact on what we see every day and this overall effects our communication (Valsesia 1). Social media feeds us information at our fingertips, whether it be true or false. Valsesia is trying to get the point across that social media has no impact on our lives without the phone and therefore, having a smaller follower count is better for our communication and overall wellbeing in the first place. Leading into my next article, social media can have a huge impact on the younger generation. This leads into part of my second research question dealing with the younger generation and their communication. The article “The Impact of Social Media on Youth Mental Health: Challenges and Opportunities” by Jacqueline Nesi shows how social media is a very complex brand of information and makes it complicated for everyone. Younger kids having access to it and multiple devices like computers and phones makes it that much more difficult. There are a lot of positives and negatives for younger kids having access to social media and the internet in general. It has an impact on their mental health and studies show it leads to signs of depression, body dysmorphia, eating disorders (Nesi 2). It can also affect their communication and outward identity due to things such as bullying, internet drama, and behavioral problems. While it does have serious negative risks, social media also can bring a lot of new positive ones. Things like creative ideas, humor and entertainment, and being able to explore their identity are all really great positives that social media gives us (Nesi 4). Most of them using it as a way to connect with friends and family and help them feel a sense of acceptance and belonging (Nesi 4). Similarly to this, social media has given a great outlet for kids and young adults to speak out on issues going on in the world. The article “Building Bridges: Exploring the Communication Trends and Perceived Sociopolitical Benefits of Adolescents Engaging in Online Social Justice Efforts” by Mariah Elsa Kornbluh goes into detail about the racial injustices in the world and how they are communicated through social media. Social media networks can help connect kids to different backgrounds and aspects of their lives (Kornbluh 1). Kornbluh expresses how a society only can flourish under civic engagement and being able to express ourselves, and social media is helping us do that. It is helping the younger generation prepare for the civic role that they will undergo (Kornbluh 2). Social media helps play a major role in participating in political movements and bringing awareness to topics (Kornbluh 3). This all is done by the younger generation and would not be possible without them. So, while it is easy to look at the negative parts of social media and how it effects the younger generation, it also brings great awareness to real life problems in our world. This last article I wanted to go over dealing with this research question has to do with the pandemic. The article “Responses to COVID-19 in Higher Education: Social Media Usage for Sustaining Formal Academic Communication in Developing Countries” by Abu Elnasr E. Sobaih, Ahmed M. Hasanein and Ahmed E. Abu Elnasr briefly talks about communication with social media in higher education systems. Education systems had to switch from in person learning and communication to online learning, which was a struggle for everyone. Throughout the time that this took place, results showed that social media had a positive effect on students dealing with this (Sobaih 1). Students used social media to build a community and help support each other through this rough time. Through these results, proper usage of social media can be shown as a positive result for a new era of learning (Sobaih 1). This is just one more reason why social media can help us improve our future.

After answering my research questions, it has become clear to me that while social media does have negative aspects, the positive aspects outweigh them. Between the articles and my own research, I have enough evidence to prove that social media does effect communication, but in a more positive way. The way we act and present ourselves is heavily influenced by social media and communication between generations are different and can be seen that way. It is important to note the accomplishments we have made as a society with social media and the media in general. It has helped connect families, provide support groups, and provide entertainment in desperate times. Our communication has changed because of social media but has changed and helped us for the better in the long run. Keeping social media a positive place and staying away from the toxic people on it will only help us grow and learn new things about ourselves.

Works Cited

Boyd, Clark. “A Guide to Using Social Media in the Workplace in 2021.”  The Blueprint , The Blueprint, 13 May 2020, www.fool.com/the-blueprint/social-media-in-the-workplace/.

https://www.fool.com/the-blueprint/social-media-in-the-workplace/

D, Valdez, et al. “Social Media Insights Into US Mental Health During the Covid-19 Pandemic: Longitudinal Analysis of Twitter Data.”  Journal of Medical Internet Research  , vol. 22, no. 12, 14 Dec. 2020, pp. 1438–8871.

http://eds.b.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/detail/detail? vid=8&sid=ff59b04c-b868-44cd-b864-4538e112a2ea%40sessionmgr103&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=33284783&db=mnh

J, Nesi. “The Impact of Social Media on Youth Health: Challenges and Opportunities.”  North Carolina Medical Journal , vol. 81, no. 2, 2020, pp. 116–121.

http://eds.b.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/detail/detail?vid=10&sid=ff59b04c-b868-44cd-b864-4538e112a2ea%40sessionmgr103&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=32132255&db=mnh

Gee, James Paul. “What is literacy.”  Negotiating academic literacies: Teaching and learning  across languages and cultures  (1998): 51-59.

https://academic.jamespaulgee.com/pdfs/Gee%20What%20is%20Literacy.pdf

Hanke, Stacey. “How Social Media Affects Our Ability to Communicate.”  Thrive Global , 13  Sept. 2018, thriveglobal.com/stories/how-social-media-affects-our-ability-to-communicate/.

https://thriveglobal.com/stories/how-social-media-affects-our-ability-to-communicate/

http://eds.a.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/pdfviewer/pdfviewer?vid=4&sid=467b825c-34f8-4e47-95df-e5b2b61bbaf4%40sessionmgr4006

Kornbluh, Mariah Elsa. “Building Bridges.”  Youth & Society , vol. 51, no. 8, 2017, pp. 1104–1126., doi:10.1177/0044118×17723656.

https://journals-sagepub-com.proxy.ulib.csuohio.edu/doi/pdf/10.1177/0044118X17723656

Retchin, Sarah, et al. “The Impact of Social Media Use on Social Skills.”  New York Behavioral Health , 1 Dec. 2020, newyorkbehavioralhealth.com/the-impact-of-social-media-use-on-social-skills/.

https://newyorkbehavioralhealth.com/the-impact-of-social-media-use-on-social-skills/

Sobaih, Abu Elnasr E., et al. “Responses to COVID-19 in Higher Education: Social Media Usage for Sustaining Formal Academic Communication in Developing Countries.”  MDPI , Multidisciplinary Digital Publishing Institute, 12 Aug. 2020, www.mdpi.com/2071-1050/12/16/6520/htm.

https://www.mdpi.com/2071-1050/12/16/6520/htm

Tayeb, Seyed Mohammad, et al. “The Role of Information Systems in Communication through Social Media.”  International Journal of Data and Network Science , vol. 3, no. 3, 2019, pp. 245–268., doi:10.5267/j.ijdns.2019.2.002.

http://www.growingscience.com/ijds/Vol3/ijdns_2019_15.pdf

Valsesia, Francesca, et al. “The Positive Effect of Not Following Others on Social Media .”  Journal of Marketing Research  , vol. 57, no. 6, Dec. 2020, pp. 1152–1168.

https://www.francescavalsesia.com/uploads/1/0/5/1/105151509/the_positive_effect_of_not_following_others_on_social_media.pdf

Understanding Literacy in Our Lives by Lindsey Matier is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Writing a Social Media Essay: Tips and Examples

In an era where a single tweet can spark a global conversation and an Instagram post can redefine trends, it's fascinating to note that the average person spends approximately 2 hours and 31 minutes per day on social media platforms. That's more than 900 hours a year devoted to scrolling, liking, and sharing in the vast digital landscape. As we find ourselves deeply intertwined in the fabric of online communities, the significance of understanding and articulating the dynamics of social media through the written word, particularly in an essay on social media, becomes increasingly apparent. So, why embark on the journey of crafting an essay on this ubiquitous aspect of modern life? Join us as we unravel the layers of social media's impact, explore its nuances, and discover the art of conveying these insights through the written form.

Short Description

In this article, we'll explore how to write an essay on social media and the purpose behind these narratives while also delving into a myriad of engaging topics. From the heartbeat of online connections to the rhythm of effective storytelling, we'll guide you organically through the process, sharing insights on structure, approach, and the creative essence that makes each essay unique. And if you're seeking assistance, pondering - ' I wish I could find someone to write my essay ,' we'll also furnish example essays to empower you to tackle such tasks independently.

Why Write a Social Media Essay

In a world buzzing with hashtags, filters, and the constant hum of notifications, the idea of sitting down to craft an essay about social media might seem as out of place as a cassette tape in a streaming era. Yet, there's something oddly therapeutic, almost rebellious, about pausing in the midst of 280-character wisdom to delve deeper into the why behind our digital existence.

So, what is social media essay, and what's the purpose of writing it? Well, it's more than just an exercise in intellectual curiosity. It's a personal journey, a reflective pause in the ceaseless scroll. While writing the essay, we gain the power to articulate the intangible, to breathe life into the pixels that dance across our screens. It's an opportunity to make sense of the chaos, to find meaning in the memes, and perhaps, in the process, to uncover a bit more about ourselves in this digital wilderness.

Let's face it - our online lives are a fast-paced carousel of memes, viral challenges, and carefully curated selfies. So, why bother wrestling with words and paragraphs in a world where brevity is king? The answer lies in the art of unraveling the digital tapestry that envelops us.

There's a magic in articulating the dance between the profound and the mundane that occurs within the confines of our screens. An essay becomes a lens, focusing our attention on the subtleties of social media dynamics – the inside jokes that become global phenomena, the ripple effect of a well-timed retweet, and the silent conversations unfolding in the comment sections.

6 Key Tips for Crafting a Social Media Essay

Now that we've set sail into the realm of essays on the digital landscape, it's only fair to equip ourselves with a few trusty tools for the journey. Think of these tips as your compass, helping you navigate the sometimes choppy, often unpredictable waters of crafting an essay on social media.

• Embrace Your Authentic Voice: Just like your favorite Instagram filter can't hide the real you, your essay should reflect your genuine thoughts and feelings. Don't be afraid to let your unique voice shine through – whether it's witty, contemplative, or a delightful blend of both.
• Dive into the Details: Social media isn't just about the grand gestures; it's the small, often unnoticed details that weave the most compelling narratives. Explore the minutiae of your online experiences – the peculiar hashtags, the quirky bios, and the unexpected connections that leave a lasting imprint.
• Craft Your Hashtag Haiku: Much like poetry, brevity can be your ally in social media essays. Think of hashtags as haikus – succinct, impactful, and capable of conveying a universe of meaning in just a few characters. Choose them wisely.
• Engage with the Comments Section: The comments section is the lively pub where digital conversations unfold. Dive in, clink glasses, and engage with the diverse perspectives swirling around. It's in these interactions that the real magic happens – where ideas collide, evolve, and sometimes, transform.
• Navigate the Memescape: Memes are the folklore of the digital age, carrying tales of humor, irony, and cultural resonance. Don't shy away from exploring the memescape in your essay. Unravel the layers, decipher the symbolism, and appreciate the humor that often holds up a mirror to society.
• Be Mindful of the Clickbait Pitfalls: While clickbait might be the flashy neon sign on the digital highway, it's essential to tread carefully. Ensure your essay isn't just a sensational headline but a thoughtful exploration that goes beyond the surface.

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Social Media Essay Structure

In the age of viral tweets and digital conversations, tackling the essay format is more than just stringing words together—it's about creating a roadmap. As we navigate this landscape of likes and retweets, understanding the structural foundations becomes key. So, let's cut through the noise and explore the practical aspects of how to write a social media essay that mirrors the rhythm of our online experiences.

Form an Outline

Now that we've acknowledged the importance of structure in your essay, the next step is to build a solid roadmap. Think of it like planning a road trip; you wouldn't hit the highway without a map or GPS, right? Similarly, creating an outline for your essay gives you a clear direction and ensures your thoughts flow smoothly.

So, whether you decide to order an essay online or tackle it yourself, here's a simple way to go about it:

Introduction (Where You Start):

• Briefly introduce the topic.
• State your social media essay thesis or main idea.
• Example: 'Let's begin by introducing the impact of social media on modern communication, focusing on its role in shaping opinions and fostering connections.'

Body Paragraphs (The Journey):

• Each paragraph should cover a specific social media essay argument and point.
• Use examples or evidence to support your ideas.
• Example: 'The first aspect we'll explore is how social media amplifies voices. For instance, hashtags like #ClimateAction mobilize a global audience around environmental issues.'

Transitions (Smooth Turns):

• Guide your readers from one point to the next.
• Ensure a logical flow between paragraphs.
• Example: 'Having discussed the amplification of voices, let's now shift our focus to the influence of social media in spreading information.'

Counter Arguments (Addressing Detours):

• Acknowledge different perspectives.
• Counter Arguments with evidence or reasoning.
• Example: 'While social media can be a powerful tool for connectivity, critics argue that it also contributes to the spread of misinformation. Let's explore this counterargument and analyze its validity.'

Conclusion (The Destination):

• Summarize your main points.
• Restate your thesis and leave a lasting impression.
• Example: 'In conclusion, social media serves as both a bridge and a battleground of ideas. Understanding its nuances is crucial in navigating this digital landscape.'

Creating an outline for your essay not only streamlines the writing process but also ensures your readers embark on a clear and organized journey through your insights on social media. If you're exploring more options, you might even want to buy thesis for more convenience.

Make a Social Media Essay Introduction

Begin your introduction by presenting a concise overview of the key theme or topic you're addressing. Clearly state the main purpose or argument of your essay, giving readers a roadmap for what to expect. Integrate social media essay hooks like a relevant statistic, quote, or provocative question to capture attention.

For instance, if your essay is about the impact of social media on personal relationships, you might start by mentioning a statistic on the percentage of couples who met online.

Social Media Essay Body Paragraph

Structure each social media essay body paragraph around a specific aspect of your chosen topic. Start with a clear topic sentence that encapsulates the main idea of the paragraph. Provide concrete examples, data, or case studies to support your points and strengthen your argument. Maintain a logical flow between paragraphs by using effective transitions.

If your essay focuses on the positive effects of social media on business marketing, dedicate a paragraph to showcasing successful campaigns and how they leveraged different platforms.

Social Media Essay Conclusion

In your conclusion, succinctly recap the main points discussed in the body paragraphs. Reinforce your thesis statement and emphasize its broader implications. Rather than introducing new information, use the conclusion to leave a lasting impression on your readers. Consider prompting further thought or suggesting practical applications of your findings.

For instance, if your essay examined the impact of social media on political discourse, conclude by encouraging readers to critically evaluate the information they encounter online and actively engage in constructive conversations.

Proofread and Revise

In the process of writing social media essay, proofreading and revising are indispensable steps that can significantly enhance the overall quality of your work. Begin by meticulously checking for grammatical errors, ensuring that your sentences are clear and concise. Pay attention to the flow of your ideas, confirming that each paragraph seamlessly transitions into the next.

During the proofreading phase, keep an eye out for any inconsistencies in tone or style. This is an opportunity to refine your language and ensure that it aligns with the intended voice of your essay. Look for repetitive phrases or unnecessary words that might detract from the clarity of your message.

As you revise, consider the effectiveness of your hook. Does it still resonate as strongly as you intended? Can it be tweaked to better captivate your audience? A compelling hook sets the tone for your entire essay, so invest time in perfecting this crucial element.

Furthermore, don't hesitate to seek feedback from peers or mentors. Another perspective can provide valuable insights into areas that may need improvement. Fresh eyes often catch nuances that the writer might overlook. Alternatively, you might also explore the option to buy coursework for additional support.

Social Media Essay Topics

In the vast realm of social media, where every like and share contributes to the digital narrative, choosing the right essay topic becomes a crucial compass for exploration. Let's explore thought-provoking topics that not only capture attention but also invite insightful discussions on the intricacies of our interconnected world.

Impact on Society:

• The Role of Social Media in Redefining Friendship and Social Bonds
• How Has TikTok Influenced Global Pop Culture Trends?
• The Impact of Social Media on Political Polarization
• Social Media and Mental Health: Exploring the Connection
• The Evolution of Language on Social Media Platforms
• Examining the Influence of Social Media on Body Image
• Fake News and Its Proliferation on Social Media
• Social Media and the Rise of Influencer Marketing
• The Intersection of Social Media and Dating Apps
• Has Social Media Narrowed or Expanded Cultural Perspectives?
• The Role of Social Media in Fostering Global Communities
• The Influence of Social Media on Consumer Behavior
• Analyzing the Impact of Social Media on News Consumption
• The Rise of 'Cancel Culture' on Social Media Platforms
• Social Media and Its Role in Spreading Disinformation
• The Impact of Social Media on Language and Communication Skills
• Social Media and its Influence on Political Movements
• The Relationship Between Social Media Use and Sleep Patterns
• Social Media and the Accessibility of Educational Resources
• The Cultural Significance of Memes on Social Media

Individual and Identity:

• The Impact of Social Media Addiction on Personal Relationships and Intimacy
• Self-Expression and Authenticity on Social Networking Sites
• Social Media and Its Influence on Teenage Identity Formation
• The Role of Social Media in Shaping Beauty Standards
• Navigating Online Dating and Relationships in the Social Media Age
• The Impact of Social Media on Parenting Styles
• Social Media and Its Influence on Body Positivity Movements
• The Perception of Success: Social Media's Role in Achievement Culture
• Social Media and the Construction of Online Persona vs. Real Self
• Social Media and Its Influence on Lifestyle Choices
• The Role of Social Media in Shaping Career Aspirations
• The Intersection of Mental Health Narratives and Social Media
• The Impact of Social Media on Self-Esteem and Well-Being
• How Social Media Influences Gender Identity and Expression
• Exploring the Concept of Digital Detox in the Social Media Era
• The Role of Social Media in Shaping Cultural Identity
• The Connection Between Social Media and Impulse Buying
• Social Media and Its Influence on Dietary Choices
• Balancing Privacy and Self-Disclosure on Social Media
• The Impact of Social Media on Friendships Over Time

Digital Activism and Advocacy:

• The Effectiveness of Hashtag Movements in Promoting Social Change
• Social Media and Its Role in Amplifying Underrepresented Voices
• The Impact of Social Media on Global Environmental Activism
• Online Activism: The Evolution from Clicktivism to Concrete Action
• The Role of Social Media in Advancing LGBTQ+ Rights
• Social Media and Its Impact on Anti-Racism Movements
• Analyzing the Challenges of Digital Advocacy in Authoritarian Regimes
• Social Media and the Global Fight Against Cyberbullying
• The Intersection of Social Media and Mental Health Advocacy
• Examining the Role of Social Media in Humanitarian Campaigns
• Crowdsourcing for Change: How Social Media Fuels Fundraising
• The Challenges of Digital Activism in the Age of Information Overload
• Social Media and Its Impact on Disability Advocacy
• The Role of Social Media in Combating Gender-Based Violence
• Online Petitions and Their Influence on Policy Change
• Exploring the Intersection of Social Media and Animal Rights Activism
• The Impact of Social Media on Indigenous Rights Advocacy
• Digital Advocacy and Its Role in Healthcare Reform
• Social Media's Influence on Youth Activism
• Navigating Challenges in Allyship on Social Media Platforms

Privacy and Ethics:

• The Implications of Facial Recognition Technology on Social Media
• Social Media Platforms and the Ethics of User Data Collection
• The Role of Social Media in Combating Deepfakes
• Balancing Freedom of Speech and Moderation on Social Media
• Social Media and the Challenges of Regulating Disinformation
• Ethical Considerations in Targeted Advertising on Social Media
• The Impact of Social Media Algorithms on User Behavior
• Social Media and the Right to Privacy: Where to Draw the Line?
• The Influence of Social Media on Political Manipulation and Propaganda
• Data Security Concerns in the Era of Social Media
• The Ethics of Social Media Influencer Marketing
• Social Media and Its Role in Combating Cyberbullying
• The Impact of Social Media on Juror Bias in Legal Cases
• Exploring the Ethics of Incorporating Social Media Usage in Hiring Decisions by Employers
• Social Media and Its Role in Combating Hate Speech
• Balancing Personalization with Privacy in Social Media Websites
• The Influence of Social Media on Public Perceptions of Law Enforcement
• Social Media and the Challenges of Content Moderation
• Addressing Online Harassment: Ethical Considerations for Platforms
• The Responsibility of Social Media Platforms in Protecting User Privacy

Future Trends and Innovations:

• The Future of Social Media: Emerging Platforms and Trends
• The Role of Augmented Reality (AR) in Shaping the Future of Social Media
• Virtual Reality (VR) and Its Potential Impact on Social Media Engagement
• The Rise of NFTs (Non-Fungible Tokens) and Social Media
• Social Media and the Evolution of Live Streaming Culture
• The Impact of Voice Search and Voice Assistants on Social Media
• Social Commerce: The Future of E-Commerce Through Social Media
• Exploring the Influence of Artificial Intelligence (AI) on Social Media
• The Role of Blockchain Technology in Enhancing Social Media Security
• Social Media and the Integration of Virtual Influencers
• The Future of Social Media Content: Short-Form vs. Long-Form
• The Influence of User-Generated Content on Future Social Media Trends
• Social Media and the Adoption of 5G Technology
• The Potential of Gamification in Shaping Social Media Engagement
• The Impact of Social Media on the Future of Work and Remote Collaboration
• Exploring the Relationship Between Social Media and Mental Health Apps
• The Influence of User Privacy Concerns on Future Social Media Developments
• Social Media and the Role of Ephemeral Content in Communication
• The Intersection of Social Media and Virtual Events
• Predicting the Next Wave of Social Media Influencer Trends

If these topics piqued your interest, you'll likely find persuasive essay topics equally fascinating! Dive into our article for a variety of options that might just spark your curiosity and inspire your next writing venture.

Social Media Essay Example

Crafting a standout essay isn't just about the words; it's about weaving a narrative that grabs your reader's attention. Before we say our goodbyes, why not take a peek at our sample essays? Our seasoned writers poured their expertise into creating persuasive pieces, offering you insights into both how to write an essay on social media and the kind of polished language that can elevate your own writing.

Wrapping Up

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Social media's growing impact on our lives

Media psychology researchers are beginning to tease apart the ways in which time spent on social media is, and is not, impacting our day-to-day lives.

Social media use has skyrocketed over the past decade and a half. Whereas only five percent of adults in the United States reported using a social media platform in 2005, that number is now around 70 percent .

Growth in the number of people who use Facebook, Instagram, Twitter, and Snapchat and other social media platforms — and the time spent on them—has garnered interest and concern among policymakers, teachers, parents, and clinicians about social media's impacts on our lives and psychological well-being.

While the research is still in its early years — Facebook itself only celebrated its 15 th birthday this year — media psychology researchers are beginning to tease apart the ways in which time spent on these platforms is, and is not, impacting our day-to-day lives.

Social media and relationships

One particularly pernicious concern is whether time spent on social media sites is eating away at face-to-face time, a phenomenon known as social displacement .

Fears about social displacement are longstanding, as old as the telephone and probably older. “This issue of displacement has gone on for more than 100 years,” says Jeffrey Hall, PhD, director of the Relationships and Technology Lab at the University of Kansas. “No matter what the technology is,” says Hall, there is always a “cultural belief that it's replacing face-to-face time with our close friends and family.”

Hall's research interrogates that cultural belief. In one study , participants kept a daily log of time spent doing 19 different activities during weeks when they were and were not asked to abstain from using social media. In the weeks when people abstained from social media, they spent more time browsing the internet, working, cleaning, and doing household chores. However, during these same abstention periods, there was no difference in people's time spent socializing with their strongest social ties.

The upshot? “I tend to believe, given my own work and then reading the work of others, that there's very little evidence that social media directly displaces meaningful interaction with close relational partners,” says Hall. One possible reason for this is because we tend to interact with our close loved ones through several different modalities—such as texts, emails, phone calls, and in-person time.

What about teens?

When it comes to teens, a recent study by Jean Twenge , PhD, professor of psychology at San Diego State University, and colleagues found that, as a cohort, high school seniors heading to college in 2016 spent an “ hour less a day engaging in in-person social interaction” — such as going to parties, movies, or riding in cars together — compared with high school seniors in the late 1980s. As a group, this decline was associated with increased digital media use. However, at the individual level, more social media use was positively associated with more in-person social interaction. The study also found that adolescents who spent the most time on social media and the least time in face-to-face social interactions reported the most loneliness.

While Twenge and colleagues posit that overall face-to-face interactions among teens may be down due to increased time spent on digital media, Hall says there's a possibility that the relationship goes the other way.

Hall cites the work of danah boyd, PhD, principal researcher at Microsoft Research  and the founder of Data & Society . “She [boyd] says that it's not the case that teens are displacing their social face-to-face time through social media. Instead, she argues we got the causality reversed,” says Hall. “We are increasingly restricting teens' ability to spend time with their peers . . . and they're turning to social media to augment it.”

According to Hall, both phenomena could be happening in tandem — restrictive parenting could drive social media use and social media use could reduce the time teens spend together in person — but focusing on the latter places the culpability more on teens while ignoring the societal forces that are also at play.

The evidence is clear about one thing: Social media is popular among teens. A 2018 Common Sense Media report found that 81 percent of teens use social media, and more than a third report using social media sites multiple times an hour. These statistics have risen dramatically over the past six years, likely driven by increased access to mobile devices. Rising along with these stats is a growing interest in the impact that social media is having on teen cognitive development and psychological well-being.

“What we have found, in general, is that social media presents both risks and opportunities for adolescents,” says Kaveri Subrahmanyam, PhD, a developmental psychologist, professor at Cal State LA, and associate director of the Children's Digital Media Center, Los Angeles .

Risks of expanding social networks

Social media benefits teens by expanding their social networks and keeping them in touch with their peers and far-away friends and family. It is also a creativity outlet. In the Common Sense Media report, more than a quarter of teens said that “social media is ‘extremely' or ‘very' important for them for expressing themselves creatively.”

But there are also risks. The Common Sense Media survey found that 13 percent of teens reported being cyberbullied at least once. And social media can be a conduit for accessing inappropriate content like violent images or pornography. Nearly two-thirds of teens who use social media said they “'often' or ‘sometimes' come across racist, sexist, homophobic, or religious-based hate content in social media.”

With all of these benefits and risks, how is social media affecting cognitive development? “What we have found at the Children's Digital Media Center is that a lot of digital communication use and, in particular, social media use seems to be connected to offline developmental concerns,” says Subrahmanyam. “If you look at the adolescent developmental literature, the core issues facing youth are sexuality, identity, and intimacy,” says Subrahmanyam.

Her research suggests that different types of digital communication may involve different developmental issues. For example, she has found that teens frequently talked about sex in chat rooms , whereas their use of blogs and social media appears to be more concerned with self-presentation and identity construction.

In particular, exploring one's identity appears to be a crucial use of visually focused social media sites for adolescents. “Whether it's Facebook, whether it's Instagram, there's a lot of strategic self presentation, and it does seem to be in the service of identity,” says Subrahmanyam. “I think where it gets gray is that we don't know if this is necessarily beneficial or if it harms.”

Remaining questions

“It's important to develop a coherent identity,” she says. “But within the context of social media — when it's not clear that people are necessarily engaging in real self presentation and there's a lot of ideal-self or false-self presentation — is that good?”

There are also more questions than answers when it comes to how social media affects the development of intimate relationships during adolescence. Does having a wide network of contacts — as is common in social media—lead to more superficial interactions and hinder intimacy? Or, perhaps more important, “Is the support that you get online as effective as the support that you get offline?” ponders Subrahmanyam. “We don't know that necessarily.”

Based on her own research comparing text messages and face-to-face interactions, she says: “My hypothesis is that maybe digital interactions may be a little more ephemeral, they're a little more fleeting, and you feel good, but that the feeling is lost quickly versus face-to-face interaction.”

However, she notes that today's teens — being tech natives — may get less hung up on the online/offline dichotomy. “ We tend to think about online and offline as disconnected, but we have to recognize that for youth . . . there's so much more fluidity and connectedness between the real and the physical and the offline and the online,” she says.

In fact, growing up with digital technology may be changing teen brain development in ways we don't yet know — and these changes may, in turn, change how teens relate to technology. “Because the exposure to technology is happening so early, we have to be mindful of the possibility that perhaps there are changes happening at a neural level with early exposure,” says Subrahmanyam. “How youths interact with technology could just be qualitatively different from how we do it.”

In part two of this article , we will look at how social media affects psychological well-being and ways of using social media that are likely to amplify its benefits and decrease its harms.

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• Open access
• Published: 06 July 2023

Pros & cons: impacts of social media on mental health

• Ágnes Zsila 1 , 2 &
• Marc Eric S. Reyes   ORCID: orcid.org/0000-0002-5280-1315 3  

BMC Psychology volume  11 , Article number:  201 ( 2023 ) Cite this article

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The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

Statista. (2022). Time spent on social media [Chart]. Accessed June 14, 2023, from https://www.statista.com/chart/18983/time-spent-on-social-media/ .

Pew Research Center. (2023). Teens and social media: Key findings from Pew Research Center surveys. Retrieved June 14, 2023, from https://www.pewresearch.org/short-reads/2023/04/24/teens-and-social-media-key-findings-from-pew-research-center-surveys/ .

Boer, M., Van Den Eijnden, R. J., Boniel-Nissim, M., Wong, S. L., Inchley, J. C.,Badura, P.,… Stevens, G. W. (2020). Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health , 66(6), S89-S99. https://doi.org/10.1016/j.jadohealth.2020.02.011.

Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:2208. https://doi.org/10.3389/fpubh.2021.641831 .

Article   Google Scholar  

Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci. 2020;5:245–57. https://doi.org/10.1007/s41347-020-00094-8 .

Article   PubMed   PubMed Central   Google Scholar  

Harriger JA, Thompson JK, Tiggemann M. TikTok, TikTok, the time is now: future directions in social media and body image. Body Image. 2023;44:222–6. https://doi.org/10.1016/j.bodyim.2021.12.005 .

Article   PubMed   Google Scholar  

Chi LC, Tang TC, Tang E. The phubbing phenomenon: a cross-sectional study on the relationships among social media addiction, fear of missing out, personality traits, and phubbing behavior. Curr Psychol. 2022;41(2):1112–23. https://doi.org/10.1007/s12144-022-0135-4 .

Valkenburg PM. Social media use and well-being: what we know and what we need to know. Curr Opin Psychol. 2022;45:101294. https://doi.org/10.1016/j.copsyc.2020.101294 .

Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, Urbán R, Farkas J, Rigó P Jr, Demetrovics Z. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. 2017;12(1):e0169839. https://doi.org/10.1371/journal.pone.0169839 .

American Psychological Association. (2023). APA panel issues recommendations for adolescent social media use. Retrieved from https://apa-panel-issues-recommendations-for-adolescent-social-media-use-774560.html .

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Acknowledgements

Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

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Zsila, Á., Reyes, M.E.S. Pros & cons: impacts of social media on mental health. BMC Psychol 11 , 201 (2023). https://doi.org/10.1186/s40359-023-01243-x

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Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

John a. naslund.

a Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA

Ameya Bondre

b CareNX Innovations, Mumbai, India

John Torous

c Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA

Kelly A. Aschbrenner

d Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH

Social media platforms are popular venues for sharing personal experiences, seeking information, and offering peer-to-peer support among individuals living with mental illness. With significant shortfalls in the availability, quality, and reach of evidence-based mental health services across the United States and globally, social media platforms may afford new opportunities to bridge this gap. However, caution is warranted, as numerous studies highlight risks of social media use for mental health. In this commentary, we consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services. Specifically, we summarize current research on the use of social media among mental health service users, and early efforts using social media for the delivery of evidence-based programs. We also review the risks, potential harms, and necessary safety precautions with using social media for mental health. To conclude, we explore opportunities using data science and machine learning, for example by leveraging social media for detecting mental disorders and developing predictive models aimed at characterizing the aetiology and progression of mental disorders. These various efforts using social media, as summarized in this commentary, hold promise for improving the lives of individuals living with mental disorders.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos ( Ahmed, Ahmad, Ahmad, & Zakaria, 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals, to upwards of 97% among younger individuals ( Aschbrenner, Naslund, Grinley, et al., 2018 ; M. L. Birnbaum, Rizvi, Correll, Kane, & Confino, 2017 ; Brunette et al., 2019 ; Naslund, Aschbrenner, & Bartels, 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges ( Bucci, Schwannauer, & Berry, 2019 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016b ).

Across the United States and globally, very few people living with mental illness have access to adequate mental health services ( Patel et al., 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health ( Orben & Przybylski, 2019 ), and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media”, and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population ( We Are Social, 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones ( Firth et al., 2015 ; Glick, Druss, Pina, Lally, & Conde, 2016 ; Torous, Chan, et al., 2014 ; Torous, Friedman, & Keshavan, 2014 ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals ( Trefflich, Kalckreuth, Mergl, & Rummel-Kluge, 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites ( Miller, Stewart, Schrimsher, Peeples, & Buckley, 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared to low-income groups from the general population ( Brunette et al., 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants ( Naslund, Aschbrenner, & Bartels, 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media ( Aschbrenner, Naslund, Grinley, et al., 2018 ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study ( Abdel-Baki, Lal, D.-Charron, Stip, & Kara, 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI), and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 hours each day ( Gay, Torous, Joseph, Pandya, & Duckworth, 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 hours per day ( M. L. Birnbaum et al., 2017 ). Similarly, in a sample of adolescents ages 13-18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat ( Aschbrenner et al., 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: 1) Facilitate social interaction; 2) Access to a peer support network; and 3) Promote engagement and retention in services.

Summary of potential benefits and challenges with social media for mental health

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals ( Torous & Keshavan, 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily ( Miller et al., 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions ( Berger, Wagner, & Baker, 2005 ), such as serious mental disorders ( Highton-Williamson, Priebe, & Giacco, 2015 ).

Studies have found that individuals with serious mental disorders ( Spinzy, Nitzan, Becker, Bloch, & Fennig, 2012 ) as well as young adults with mental illness ( Gowen, Deschaine, Gruttadara, & Markey, 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world, and also experience high rates of loneliness ( Badcock et al., 2015 ; Giacco, Palumbo, Strappelli, Catapano, & Priebe, 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone ( Brusilovskiy, Townley, Snethen, & Salzer, 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated ( Gowen et al., 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections ( Brusilovskiy et al., 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person ( Rideout & Fox, 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters ( Batterham & Calear, 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges, and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information ( Schrank, Sibitz, Unger, & Amering, 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations ( Docherty et al., 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction ( Kiesler, Siegel, & McGuire, 1984 ), with interactions being more fluid, and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction ( Indian & Grieve, 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect and attentional impairment, as well as active social avoidance due to hallucinations or other concerns ( Hansen, Torgalsbøen, Melle, & Bell, 2009 ); thus, potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support ( Bucci et al., 2019 ; Naslund, Aschbrenner, et al., 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges ( Davidson, Chinman, Sells, & Rowe, 2006 ; Mead, Hilton, & Curtis, 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure, and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication ( Haker, Lauber, & Rössler, 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness ( Vayreda & Antaki, 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al (2015) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience ( Highton-Williamson et al., 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness ( Naslund, Grande, Aschbrenner, & Elwyn, 2014 ). In another study, Chang (2009) delineated various communication patterns in an online psychosis peer-support group ( Chang, 2009 ). Specifically, different forms of support emerged, including ‘informational support’ about medication use or contacting mental health providers, ‘esteem support’ involving positive comments for encouragement, ‘network support’ for sharing similar experiences, and ‘emotional support’ to express understanding of a peer’s situation and offer hope or confidence ( Chang, 2009 ). Bauer et al. (2013) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group ( Bauer, Bauer, Spiessl, & Kagerbauer, 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. (2017) found that this served as an important opportunity to seek support and to hear about the experiences of others ( Berry et al., 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media ( Naslund et al., 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared ( Saha et al., 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information ( Lal, Nguyen, & Theriault, 2018 ), connecting with mental health providers ( M. L. Birnbaum et al., 2017 ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing ( Naslund et al., 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al (2018) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions, and may also improve perceived social support ( Biagianti, Quraishi, & Schlosser, 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis ( Alvarez-Jimenez et al., 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process ( Alvarez-Jimenez et al., 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services ( Alvarez-Jimenez et al., 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis ( Alvarez-Jimenez et al., 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools ( Schlosser et al., 2016 ). This unique approach to the design of the app is aimed at promoting engagement, and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia ( Schlosser et al., 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies ( Aschbrenner, Naslund, Shevenell, Kinney, & Bartels, 2016 ; Aschbrenner, Naslund, Shevenell, Mueser, & Bartels, 2016 ). The intervention holds tremendous promise as lack of support is one of the largest barriers toward exercise in patients with serious mental illness ( Firth et al., 2016 ) and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals ( Aschbrenner, Naslund, & Bartels, 2016 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016a ). To date, this program has demonstrate preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group ( Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016 ), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program ( Naslund, Aschbrenner, Marsch, McHugo, & Bartels, 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from a variety of real world community mental health services settings ( Aschbrenner, Naslund, Gorin, et al., 2018 ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway ( Alvarez-Jimenez et al., 2019 ; Aschbrenner, Naslund, Gorin, et al., 2018 ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services ( Gleeson et al., 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and well being, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem, and opportunities for self-disclosure ( Best, Manktelow, & Taylor, 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms and bullying ( Best et al., 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: 1) Impact on symptoms; 2) Facing hostile interactions; and 3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people ( Andreassen et al., 2016 ; Kross et al., 2013 ; Woods & Scott, 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented ( Stiglic & Viner, 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media ( Rideout & Fox, 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms ( Feinstein et al., 2013 ). Still, the cross sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences ( Orben & Przybylski, 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms ( Lin et al., 2016 ). More time spent using social media is also associated with greater symptoms of anxiety ( Vannucci, Flannery, & Ohannessian, 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health ( Primack et al., 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared to respondents using only 2 or fewer platforms, there was a 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms ( Primack et al., 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people ( Twenge & Campbell, 2018 ), and may contribute to greater loneliness ( Bucci et al., 2019 ), and negative effects on other aspects of health and wellbeing ( Woods & Scott, 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there was significantly greater depressive symptoms and increased risk of suicide when compared to adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities ( Twenge, Joiner, Rogers, & Martin, 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders ( Mittal, Tessner, & Walker, 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood ( Berry, Emsley, Lobban, & Bucci, 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies ( Orben & Przybylski, 2019 ), and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared to random hostile comments posted online ( Hamm et al., 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people ( Hamm et al., 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the United States, where females were twice as likely to be victims of cyberbullying compared to males ( Alhajji, Bass, & Dai, 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety ( Hamm et al., 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time ( Machmutow, Perren, Sticca, & Alsaker, 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there was over 3 times greater odds of facing online harassment in the last year compared to youth who reported mild or no depressive symptoms ( Ybarra, 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media, and in particular, were more likely to report having faced hostile comments, or being “trolled”, from others when compared to respondents without depressive symptoms (31% vs. 14%) ( Rideout & Fox, 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses ( Goodman et al., 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media ( Saha et al., 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr and other forums across 127 countries ( Sumner et al., 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online ( Torous & Keshavan, 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media, and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source ( Moorhead et al., 2013 ; Ventola, 2014 ). For persons living with mental illness there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media ( Naslund & Aschbrenner, 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt ( Naslund & Aschbrenner, 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion, or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while also highlighting that there could also be benefits. For individuals with mental illness who use social media, being aware of the risks is an essential first step, and then highlighting ways that use of these popular platforms could also contribute to some benefits, ranging from finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media, and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the United States found that female respondents were more likely to search online for information about depression or anxiety, and to try to connect with other people online who share similar mental health concerns, when compared to male respondents ( Rideout & Fox, 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information ( Rideout & Fox, 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males ( Booker, Kelly, & Sacker, 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual or transgender individuals frequently use social media for searching for health information and may be more likely compared to heterosexual individuals to share their own personal health experiences with others online ( Rideout & Fox, 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and more likely to experience online victimization when compared to heterosexual individuals ( Mereish, Sheskier, Hawthorne, & Goldbach, 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the United States ( Tynes, Willis, Stewart, & Hamilton, 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups ( Schueller, Hunter, Figueroa, & Aguilera, 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system ( Naslund et al., 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media-like features would have been omitted. Though it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature”, because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the United States, as well as from other higher income settings such as Australia or the United Kingdom. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide ( Naslund et al., 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as ‘digital phenotyping’ aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention ( Jain, Powers, Hawkins, & Brownstein, 2015 ; Onnela & Rauch, 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related ( Torous et al., 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms ( Shatte, Hutchinson, & Teague, 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health ( Manikonda & De Choudhury, 2017 ; Reece et al., 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression ( De Choudhury, Gamon, Counts, & Horvitz, 2013 ) as well as detecting users’ mood and affective states ( De Choudhury, Gamon, & Counts, 2012 ), while photos posted to Instagram can yield insights for predicting depression ( Reece & Danforth, 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared to a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns ( Michael L Birnbaum, Ernala, Rizvi, De Choudhury, & Kane, 2017 ), including more frequent discussion of tobacco use ( Hswen et al., 2017 ), symptoms of depression and anxiety ( Hswen, Naslund, Brownstein, & Hawkins, 2018b ), and suicide ( Hswen, Naslund, Brownstein, & Hawkins, 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala, Rizvi, Birnbaum, Kane, & De Choudhury, 2017). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive ‘digital phenotype’ to predict relapse and identify high-risk health behaviors among individuals living with mental illness ( Torous et al., 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary ( Chancellor, Birnbaum, Caine, Silenzio, & De Choudhury, 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users ( Bidargaddi et al., 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness ( Guntuku, Yaden, Kern, Ungar, & Eichstaedt, 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content, as this could place an individual at risk of harm or divulge sensitive health information ( Webb et al., 2017 ; Williams, Burnap, & Sloan, 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, as well as the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings ( Chancellor et al., 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media, and offer recommendations to promote safe use of these sites, while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients, while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers ( Hilty, Chan, Torous, Luo, & Boland, 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services, and coping with symptoms ( Bucci et al., 2019 ; Highton-Williamson et al., 2015 ; Naslund, Aschbrenner, et al., 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the United States and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Acknowledgements

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Conflict of Interest

The authors have nothing to disclose.

• Abdel-Baki A, Lai S, D.-Charron O, Stip E, & Kara N, (2017). Understanding access and use of technology among youth with first - episode psychosis to inform the development of technology - enabled therapeutic interventions . Early intervention in psychiatry , 77 ( 1 ), 72–76. [ PubMed ] [ Google Scholar ]
• Ahmed YA, Ahmad MN, Ahmad N, & Zakaria NH (2019). Social media for knowledge-sharing: A systematic literature review . Telematics and informatics , 37 , 72–112 . [ Google Scholar ]
• Alhajji M, Bass S, & Dai T (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 Youth Risk Behavior Survey . Global pediatric health , 6 , 2333794X19868887. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Bendall S, Koval P, Rice S, Cagliarini D, Valentine L, … Penn DL (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services . BMJ open , 9 ( 2 ), e024104. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Bendall S, Lederman R, Wadley G, Chinnery G, Vargas S, … Gleeson JF (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis . Schizophrenia research , 143 ( 1 ), 143–149. [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Gleeson J, Bendall S, Penn D, Yung A, Ryan R, … Miles C (2018). Enhancing social functioning in young people at Ultra High Risk (UHR) for psychosis: A pilot study of a novel strengths and mindfulness-based online social therapy . Schizophrenia research , 202 , 369–377. [ PubMed ] [ Google Scholar ]
• Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, & Pallesen S (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study . Psychology of Addictive Behaviors , 30 ( 2 ), 252. [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, & Bartels SJ (2016). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness . Psychiatric rehabilitation journal , 39 ( 4 ), 328. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Gorin AA, Mueser KT, Scherer EA, Viron M, … Bartels SJ, (2018). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: Protocol for a randomized controlled trial . Contemporary clinical trials , 74 , 97–106. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Grinley T, Bienvenida JCM, Bartels SJ, & Brunette M (2018). A Survey of Online and Mobile Technology Use at Peer Support Agencies . Psychiatric Quarterly , 1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Shevenell M, Kinney E, & Bartels SJ (2016). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness . The Journal of nervous and mental disease , 204 ( 6 ), 483–486. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Shevenell M, Mueser KT, & Bartels SJ (2016). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness . Psychiatric Quarterly , 57 ( 3 ), 401–415. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Tomlinson EF, Kinney A, Pratt SI, & Brunette MF (2019). Adolescents’ Use of Digital Technologies and Preferences for Mobile Health Coaching in Mental Health Settings . Frontiers in Public Health . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Badcock JC, Shah S, Mackinnon A, Stain HJ, Galletly C, Jablensky A, & Morgan VA (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile . Schizophrenia research , 169 ( 1-3 ), 268–273. [ PubMed ] [ Google Scholar ]
• Batterham PJ, & Calear AJ (2017). Preferences for internet-based mental health interventions in an adult online sample: Findings from ann online community survey . JMIR mental health , 4 ( 2 ), e26. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bauer R, Bauer M, Spiessl H, & Kagerbauer T (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder) . Nordic journal of psychiatry , 67 ( 3 ), 185–190. [ PubMed ] [ Google Scholar ]
• Berger M, Wagner TH, & Baker LC (2005). Internet use and stigmatized illness . Social science & medicine , 67 ( 8 ), 1821–1827. [ PubMed ] [ Google Scholar ]
• Berry N, Emsley R, Lobban F, & Bucci S (2018). Social media and its relationship with mood, self - esteem and paranoia in psychosis . Acta Psychiatrica Scandinavica , 138 , 558–570. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Berry N, Lobban F, Belousov M, Emsley R, Nenadic G, & Bucci S (2017). # Why We Tweet MH: understanding why people use Twitter to discuss mental health problems . Journal of medical Internet research , 19 ( 4 ), e107. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Best P, Manktelow R, & Taylor B (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review . Children and Youth Services Review , 41 , 27–36. [ Google Scholar ]
• Biagianti B, Quraishi SH, & Schlosser DA (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review . Psychiatric Services , 69 ( 4 ), 377–388. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bidargaddi N, Musiat P, Makinen V-P, Ermes M, Schrader G, & Licinio J (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies . Molecular psychiatry , 22 ( 2 ), 164. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Birnbaum ML, Emala SK, Rizvi AF, De Choudhury M, & Kane JM (2017). A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals . Journal of medical Internet research , 79 ( 8 ), e289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Birnbaum ML, Rizvi AF, Correll CU, Kane JM, & Confino J (2017). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non - psychotic mood disorders . Early intervention in psychiatry , 77 ( 4 ), 290–295. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Booker CL, Kelly YJ, & Sacker A (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK . BMC public health , 18 ( 1 ), 321. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brunette M, Achtyes E, Pratt S, Stilwell K, Opperman M, Guarino S, & Kay-Lambkin F (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention . Community mental health journal , 1–6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brusilovskiy E, Townley G, Snethen G, & Salzer MS (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses . Computers in Human Behavior , 65 , 232–240. [ Google Scholar ]
• Bucci S, Schwannauer M, & Berry N (2019). The digital revolution and its impact on mental health care . Psychology and Psychotherapy: Theory, Research and Practice , 1–21. [ PubMed ] [ Google Scholar ]
• Chancellor S, Birnbaum ML, Caine ED, Silenzio V, & De Choudhury M (2019). A taxonomy of ethical tensions in inferring mental health states from social media . Paper presented at the Proceedings of the Conference on Fairness, Accountability, and Transparency. [ Google Scholar ]
• Chang HJ (2009). Online supportive interactions: Using a network approach to examine communication patterns within a psychosis social support group in Taiwan . Journal of the American Society for Information Science and Technology , 60 ( 7 ), 1504–1517. [ Google Scholar ]
• Davidson L, Chinman M, Sells D, & Rowe M (2006). Peer support among adults with serious mental illness: a report from the field . Schizophrenia bulletin , 32 ( 3 ), 443–450. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• De Choudhury M, Gamon M, & Counts S (2012). Happy, nervous or surprised? classification of human affective states in social media . Paper presented at the Sixth International AAAI Conference on Weblogs and Social Media. [ Google Scholar ]
• De Choudhury M, Gamon M, Counts S, & Horvitz E (2013). Predicting Depression via Social Media . Paper presented at the Association for the Advancement of Artificial Intelligence. [ Google Scholar ]
• Docherty NM, Hawkins KA, Hoffman RE, Quinlan DM, Rakfeldt J, & Sledge WH (1996). Working memory, attention, and communication disturbances in schizophrenia . Journal of Abnormal Psychology , 105 ( 2 ), 212. [ PubMed ] [ Google Scholar ]
• Emala SK, Rizvi AF, Birnbaum ML, Kane JM, & De Choudhury M (2017). Linguistic Markers Indicating Therapeutic Outcomes of Social Media Disclosures of Schizophrenia . Proc. ACMHum.-Comput. Interact , 1 ( 1 ), 43. [ Google Scholar ]
• Feinstein BA, Hershenberg R, Bhatia V, Latack JA, Meuwly N, & Davila J (2013). Negative social comparison on Facebook and depressive symptoms: Rumination as a mechanism . Psychology of Popular Media Culture , 2 ( 3 ), 161. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Firth J, Cotter J, Torous J, Bucci S, Firth JA, & Yung AR (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies . Schizophrenia bulletin , 42 ( 2 ), 448–455. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Firth J, Rosenbaum S, Stubbs B, Gorczynski P, Yung AR, & Vancampfort D (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis . Psychological medicine , 46 ( 14 ), 2869–2881. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gay K, Torous J, Joseph A, Pandya A, & Duckworth K (2016). Digital technology use among individuals with schizophrenia: results of an online survey . JMIR mental health , 3 ( 2 ), el5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Giacco D, Palumbo C, Strappelli N, Catapano F, & Priebe S (2016). Social contacts and loneliness in people with psychotic and mood disorders . Comprehensive Psychiatry , 66 , 59–66. [ PubMed ] [ Google Scholar ]
• Gleeson J, Lederman R, Herrman H, Koval P, Eleftheriadis D, Bendall S, … Alvarez-Jimenez M (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial . Trials , 75 ( 1 ), 27. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Glick G, Druss B, Pina J, Lally C, & Conde M (2016). Use of mobile technology in a community mental health setting . Journal of telemedicine and telecare , 22 ( 7 ), 430–435. [ PubMed ] [ Google Scholar ]
• Goodman LA, Thompson KM, Weinfurt K, Corl S, Acker P, Mueser KT, & Rosenberg SD (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness . Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies , 12 ( 4 ), 587–599. [ PubMed ] [ Google Scholar ]
• Gowen K, Deschaine M, Gruttadara D, & Markey D (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community . Psychiatric rehabilitation journal , 35 ( 3 ), 245–250. [ PubMed ] [ Google Scholar ]
• Guntuku SC, Yaden DB, Kern ML, Ungar LH, & Eichstaedt JC (2017). Detecting depression and mental illness on social media: an integrative review . Current Opinion in Behavioral Sciences , 18 , 43–49. [ Google Scholar ]
• Haker EL, Lauber C, & Rossler W (2005). Internet forums: a self - help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica , 112 ( 6 ), 474–477. [ PubMed ] [ Google Scholar ]
• Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P, … Hartling L (2015). Prevalence and effect of cyberbullying on children and young people: A scoping review of social media studies . JAMA pediatrics , 769 ( 8 ), 770–777. [ PubMed ] [ Google Scholar ]
• Hansen CF, Torgalsboen A-K, Melle I, & Bell MD (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes . The Journal of nervous and mental disease , 197 ( 4 ), 274–277. [ PubMed ] [ Google Scholar ]
• Highton-Williamson E, Priebe S, & Giacco D (2015). Online social networking in people with psychosis: a systematic review . International Journal of Social Psychiatry , 61 ( 1 ), 92–101. [ PubMed ] [ Google Scholar ]
• Hilty DM, Chan S, Torous J, Luo J, & Boland RJ (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues . Psychiatric Clinics , 42 ( 2 ), 513–534. [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Brownstein JS, & Hawkins JB (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study . JMIR mental health , 5 ( 4 ), e11483. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Brownstein JS, & Hawkins JB (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media . Psychiatric Quarterly , 89 ( 3 ), 569–580. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Chandrashekar P, Siegel R, Brownstein JS, & Hawkins JB (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia . Psychiatry research , 257 , 479–484. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Indian M, & Grieve R (2014). When Facebook is easier than face-to-face: social support dervied from Facebook in socially anxious individuals . Personality and Individual Differences , 59 , 102–106. [ Google Scholar ]
• Jain SH, Powers BW, Hawkins JB, & Brownstein JS (2015). The digital phenotype . Nature Biotechnology , 33 ( 5 ), 462. [ PubMed ] [ Google Scholar ]
• Kiesler S, Siegel J, & McGuire TW (1984). Social psychological aspects of computer-mediated communication . American Psychologist , 39 , 1123–1134. [ Google Scholar ]
• Kross E, Verduyn P, Demiralp E, Park J, Lee DS, Lin N, … Ybarra O (2013). Facebook use predicts declines in subjective well-being in young adults . PloS one , 5 ( 8 ), e69841. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lai S, Nguyen V, & Theriault J (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first - episode psychosis . Early intervention in psychiatry , 72 ( 3 ), 324–330. [ PubMed ] [ Google Scholar ]
• Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, … Primack BA (2016). Association between social media use and depression among US young adults . Depression and anxiety , 22 ( 4 ), 323–331. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Machmutow K, Perren S, Sticca F, & Alsaker FD (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties , 17 ( 3-4 ), 403–420. [ Google Scholar ]
• Manikonda L, & De Choudhury M (2017). Modeling and understanding visual attributes of mental health disclosures in social media . Paper presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. [ Google Scholar ]
• Mead S, Hilton D, & Curtis L (2001). Peer support: A theoretical perspective . Psychiatric rehabilitation journal , 25 ( 2 ), 134. [ PubMed ] [ Google Scholar ]
• Mereish EH, Sheskier M, Hawthorne DJ, & Goldbach JT (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation . Culture, health & sexuality , 21 ( 9 ), 985–998. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miller BJ, Stewart A, Schrimsher J, Peeples D, & Buckley PF (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use . Psychiatry research , 225 ( 3 ), 458–463. [ PubMed ] [ Google Scholar ]
• Mittal VA, Tessner KD, & Walker EF (2007). Elevated social Internet use and schizotypal personality disorder in adolescents . Schizophrenia research , 94 ( 1-3 ), 50–57. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, & Hoving C (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication . Journal of medical Internet research , 15 ( 4 ), e85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, & Aschbrenner KA (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness . Psychiatric Services , appi. ps. 201800520. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, & Bartels SJ (2016). How people living with serious mental illness use smartphones, mobile apps, and social media . Psychiatric rehabilitation journal , 39 ( 4 ), 364–367. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, & Bartels SJ (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness . Digital health , 2 , 2055207616654822. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, & Bartels SJ (2016b). The future of mental health care: peer-to-peer support and social media . Epidemiology and Psychiatric Sciences , 25 ( 2 ), 113–122. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ, & Bartels SJ (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study . Psychiatric Quarterly , 59 ( 1 ), 81–94. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, McHugo GJ, Unutzer J, Marsch LA, & Bartels SJ (2017). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness . Early intervention in psychiatry . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Gonsalves PP, Gruebner O, Pendse SR, Smith SL, Sharma A, & Raviola G (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention . Current Treatment Options in Psychiatry , 1–15. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Grande SW, Aschbrenner KA, & Elwyn G (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube . PloS one , 9 ( 10 ), e110171. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Onnela J-P, & Rauch SL (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health . Neuropsychopharmacology , 41 ( 7 ), 1691. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Orben A, & Przybylski AK (2019). The association between adolescent well-being and digital technology use . Nature Human Behaviour , 3 ( 2 ), 173. [ PubMed ] [ Google Scholar ]
• Patel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, … Eaton J (2018). The Lancet Commission on global mental health and sustainable development . The Lancet . [ PubMed ] [ Google Scholar ]
• Primack BA, Shensa A, Escobar-Viera CG, Barrett EL, Sidani JE, Colditz JB, & James AE (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults . Computers in Human Behavior , 69 , 1–9. [ Google Scholar ]
• Reece AG, & Danforth CM (2017). Instagram photos reveal predictive markers of depression . EPJ Data Science , 6 ( 1 ), 15. [ Google Scholar ]
• Reece AG, Reagan AJ, Lix KL, Dodds PS, Danforth CM, & Langer EJ (2017). Forecasting the onset and course of mental illness with Twitter data . Scientific reports , 7 ( 1 ), 13006. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rideout V, & Fox S (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf [ Google Scholar ]
• Saha K, Torous J, Ernala SK, Rizuto C, Stafford A, & De Choudhury M (2019). A computational study of mental health awareness campaigns on social media . Translational behavioral medicine . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schlosser DA, Campellone T, Kim D, Truong B, Vergani S, Ward C, & Vinogradov S (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia . JMIR research protocols , 5 ( 2 ). [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schlosser DA, Campellone TR, Truong B, Etter K, Vergani S, Komaiko K, & Vinogradov S (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia . Schizophrenia bulletin , 44 ( 5 ), 1010–1020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schrank B, Sibitz I, Unger A, & Amering M (2010). How patients with schizophrenia use the internet: qualitative study . Journal of medical Internet research , 12 ( 5 ), e70. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schueller SM, Hunter JF, Figueroa C, & Aguilera A (2019). Use of digital mental health for marginalized and underserved populations . Current Treatment Options in Psychiatry , 6 ( 3 ), 243–255. [ Google Scholar ]
• Shatte AB, Hutchinson DM, & Teague SJ (2019). Machine learning in mental health: a scoping review of methods and applications . Psychological medicine , 49 ( 9 ), 1426–1448. [ PubMed ] [ Google Scholar ]
• Spinzy Y, Nitzan U, Becker G, Bloch Y, & Fennig S (2012). Does the Internet offer social opportunities for individuals with schizophrenia? a cross-sectional pilot study . Psychiatry research , 198 ( 2 ), 319–320. [ PubMed ] [ Google Scholar ]
• Stiglic N, & Viner RM (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews . BMJopen , 9 ( 1 ), e023191. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sumner SA, Galik S, Mathieu J, Ward M, Kiley T, Bartholow B, … Mork P (2019). Temporal and geographic patterns of social media posts about an emerging suicide game . Journal of Adolescent Health . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, Chan SR, Tan SY-M, Behrens J, Mathew I, Conrad EJ, … Keshavan M (2014). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics . JMIR mental health , 1 ( 1 ), e5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, Friedman R, & Keshavan M (2014). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions . JMIR mHealth and uHealth , 2 ( 1 ), e2. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, & Keshavan M (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential . Current opinion in psychiatry , 29 ( 3 ), 190–195. [ PubMed ] [ Google Scholar ]
• Torous J, Wisniewski H, Bird B, Carpenter E, David G, Elejalde E, … Henson P (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach . Journal of Technology in Behavioral Science , 1–13. [ Google Scholar ]
• Trefflich F, Kalckreuth S, Mergl R, & Rummel-Kluge C (2015). Psychiatric patients’ internet use corresponds to the internet use of the general public . Psychiatry research , 226 , 136–141. [ PubMed ] [ Google Scholar ]
• Twenge JM, & Campbell WK (2018). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study . Preventive medicine reports , 12 , 271–283. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Twenge JM, Joiner TE, Rogers ML, & Martin GN (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time . Clinical Psychological Science , 6 ( 1 ), 3–17. [ Google Scholar ]
• Tynes BM, Willis HA, Stewart AM, & Hamilton MW (2019). Race-related traumatic events online and mental health among adolescents of color . Journal of Adolescent Health , 65 ( 3 ), 371–377. [ PubMed ] [ Google Scholar ]
• Vannucci A, Flannery KM, & Ohannessian CM (2017). Social media use and anxiety in emerging adults . Journal of affective disorders , 207 , 163–166. [ PubMed ] [ Google Scholar ]
• Vayreda A, & Antaki C (2009). Social support and unsolicited advice in a bipolar disorder online forum . Qualitative health research , 19 ( 7 ), 931–942. [ PubMed ] [ Google Scholar ]
• Ventola CL (2014). Social media and health care professionals: benefits, risks, and best practices . Pharmacy and Therapeutics , 39 ( 7 ), 491. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• We Are Social. (2020). Digital in 2020 . Retrieved from https://wearesocial.com/global-digital-report-2019
• Webb H, Jirotka M, Stahl BC, Housley W, Edwards A, Williams M, … Burnap P (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the Proceedings of the 2017 ACM on Web Science Conference. [ Google Scholar ]
• Williams ML, Burnap P, & Sloan L (2017). Towards an ethical framework for publishing Twitter data in social research: Taking into account users’ views, online context and algorithmic estimation . Sociology , 57 ( 6 ), 1149–1168. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Woods HC, & Scott H (2016). # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem . Journal of adolescence , 57 , 41–49. [ PubMed ] [ Google Scholar ]
• Ybarra ML (2004). Linkages between depressive symptomatology and Internet harassment among young regular Internet users . Cyber Psychology & Behavior , 7 ( 2 ), 247–257. [ PubMed ] [ Google Scholar ]

• Open access
• Published: 16 March 2020

Exploring the role of social media in collaborative learning the new domain of learning

• Jamal Abdul Nasir Ansari 1 &
• Nawab Ali Khan 1  

Smart Learning Environments volume  7 , Article number:  9 ( 2020 ) Cite this article

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This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education institutions across the boundary wall, a hitherto unexplained area of research. This empirical study is based on the survey of 360 students of a university in eastern India, cognising students’ perception on social media and mobile devices through collaborative learning, interactivity with peers, teachers and its significant impact on students’ academic performance. A latent variance-based structural equation model approach was followed for measurement and instrument validation. The study revealed that online social media used for collaborative learning had a significant impact on interactivity with peers, teachers and online knowledge sharing behaviour.

Additionally, interactivity with teachers, peers, and online knowledge sharing behaviour has seen a significant impact on students’ engagement which consequently has a significant impact on students’ academic performance. Grounded to this finding, it would be valuable to mention that use of online social media for collaborative learning facilitate students to be more creative, dynamic and research-oriented. It is purely a domain of knowledge.

Introduction

The explosion of Information and Communication Technology (ICT) has led to an increase in the volume and smoothness in transferring course contents, which further stimulates the appeasement of Digital Learning Communities (DLCs). The millennium and naughtiness age bracket were Information Technology (IT) centric on web space where individual and geopolitical disperse learners accomplished their e-learning goals. The Educause Center for Applied Research [ECAR] ( 2012 ) surveyed students in higher education mentioned that students are pouring the acceptance of mobile computing devices (cellphones, smartphones, and tablet) in Higher Education Institutions (HEIs), roughly 67% surveyed students accepted that mobile devices and social media play a vital role in their academic performance and career enhancement. Mobile devices and social media provide excellent educational e-learning opportunities to the students for academic collaboration, accessing in course contents, and tutors despite the physical boundary (Gikas & Grant, 2013 ). Electronic communication technologies accelerate the pace of their encroachment of every aspect of life, the educational institutions incessantly long decades to struggle in seeing the role of such devices in sharing the contents, usefulness and interactivity style. Adoption and application of mobile devices and social media can provide ample futuristic learning opportunities to the students in accessing course contents as well as interaction with peers and experts (Cavus & Ibrahim, 2008 , 2009 ; Kukulska-Hulme & Shield, 2008 ; Nihalani & Mayrath, 2010 ; Richardson & Lenarcic, 2008 , Shih, 2007 ). Recently Pew Research Center reported that 55% American teenage age bracket of 15–17 years using online social networking sites, i.e. Myspace and Facebook (Reuben, 2008 ). Social media, the fast triggering the mean of virtual communication, internet-based technologies changed the life pattern of young youth.

Use of social media and mobile devices presents both advantages as well as challenges, mostly its benefits seen in terms of accessing course contents, video clip, transfer of the instructional notes etc. Overall students feel that social media and mobile devices are the cheap and convenient tools of obtaining relevant information. Studies in western countries have confronted that online social media use for collaborative learning has a significant contribution to students’ academic performance and satisfaction (Zhu, 2012 ). The purpose of this research project was to explore how learning and teaching activities in higher education institutions were affected by the integration and application of mobile devices in sharing the resource materials, interaction with colleagues and students’ academic performance. The broad goal of this research was to contemporise the in-depth perspectives of students’ perception of mobile devices and social media in learning and teaching activities. However, this research paper paid attention to only students’ experiences, and their understanding of mobile devices and social media fetched changes and its competency in academic performance. The fundamental research question of this research was, what are the opinions of students on social media and mobile devices when it is integrating into higher education for accessing, interacting with peers.

A researcher of the University of Central Florida reported that electronic devices and social media create an opportunity to the students for collaborative learning and also allowed the students in sharing the resource materials to the colleagues (Gikas & Grant, 2013 ). The result of the eight Egyptian universities confirmed that social media have the significant impact on higher education institutions especially in term of learning tools and teaching aids, faculty members’ use of social media seen at a minimum level due to several barriers (internet accessibility, mobile devices etc.).

Social media and mobile devices allow the students to create, edit and share the course contents in textual, video or audio forms. These technological innovations give birth to a new kind of learning cultures, learning based on the principles of collective exploration and interaction (Selwyn, 2012 ). Social media the phenomena originated in 2005 after the Web2.0 existence into the reality, defined more clearly as “a group of Internet-based applications that build on the ideological and technological foundation of web 2.0 and allow creation and exchange of user-generated contents (Kaplan & Haenlein, 2010 ). Mobile devices and social media provide opportunities to the students for accessing resources, materials, course contents, interaction with mentor and colleagues (Cavus & Ibrahim, 2008 , 2009 ; Richardson & Lenarcic, 2008 ).

Social media platform in academic institutions allows students to interact with their mentors, access their course contents, customisation and build students communities (Greenhow, 2011a , 2011b ). 90% school going students currently utilise the internet consistently, with more than 75% teenagers using online networking sites for e-learning (DeBell & Chapman, 2006 ; Lenhart, Arafeh, & Smith, 2008 ; Lenhart, Madden, & Hitlin, 2005 ). The result of the focus group interview of the students in 3 different universities in the United States confirmed that use of social media created opportunities to the learners for collaborative learning, creating and engaging the students in various extra curriculum activities (Gikas & Grant, 2013 ).

Research background and hypotheses

The technological innovation and increased use of the internet for e-learning by the students in higher education institutions has brought revolutionary changes in communication pattern. A report on 3000 college students in the United States revealed that 90% using Facebook while 37% using Twitter to share the resource materials as cited in (Elkaseh, Wong, & Fung, 2016 ). A study highlighted that the usage of social networking sites in educational institutions has a practical outcome on students’ learning outcomes (Jackson, 2011 ). The empirical investigation over 252 undergraduate students of business and management showed that time spent on twitter and involvement in managing social lives and sharing information, course-related influences their performance (Evans, 2014 ).

Social media for collaborative learning, interactivity with teachers, interactivity with peers

Many kinds of research confronted on the applicability of social media and mobile devices in higher education for interaction with colleagues.90% of faculty members use some social media in courses they were usually teaching or professional purposes out of the campus life. Facebook and YouTube are the most visited sites for the professional outcomes, around 2/3rd of the all-faculty use some medium fora class session, and 30% posted contents for students engagement in reading, view materials (Moran, Seaman, & Tinti-Kane, 2011 ). Use of social media and mobile devices in higher education is relatively new phenomena, completely hitherto area of research. Research on the students of faculty of Economics at University of Mortar, Bosnia, and Herzegovina reported that social media is already used for the sharing the materials and exchanges of information and students are ready for active use of social networking site (slide share etc.) for educational purposes mainly e-learning and communication (Mirela Mabić, 2014 ).

The report published by the U.S. higher education department stated that the majority of the faculty members engaged in different form of the social media for professional purposes, use of social media for teaching international business, sharing contents with the far way students, the use of social media and mobile devices for sharing and the interactive nature of online and mobile technologies build a better learning environment at international level. Responses on 308 graduate and postgraduate students in Saudi Arabia University exhibited that positive correlation between chatting, online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with students learning (Eid & Al-Jabri, 2016 ). The quantitative study on 168 faculty members using partial least square (PLS-SEM) at Carnegie classified Doctoral Research University in the USA confirmed that perceived usefulness, external pressure and compatibility of task-technology have positive effect on social media use, the higher the degree of the perceived risk of social media, the less likely to use the technological tools for classroom instruction, the study further revealed that use of social media for collaborative learning has a positive effect on students learning outcome and satisfaction (Cao, Ajjan, & Hong, 2013 ). Therefore, the authors have hypothesized:

H1: Use of social media for collaborative learning is positively associated with interactivity with teachers.

Additionally, Madden and Zickuhr ( 2011 ) concluded that 83% of internet user within the age bracket of 18–29 years adopting social media for interaction with colleagues. Kabilan, Ahmad, and Abidin ( 2010 ) made an empirical investigation on 300 students at University Sains Malaysia and concluded that 74% students found to be the same view that social media infuses constructive attitude towards learning English (Fig. 1 ).

Research Model

Reuben ( 2008 ) concluded in his study on social media usage among professional institutions revealed that Facebook and YouTube used over half of 148 higher education institutions. Nevertheless, a recent survey of 456 accredited United States institutions highlighted 100% using some form of social media, notably Facebook 98% and Twitter 84% for e-learning purposes, interaction with mentors (Barnes & Lescault, 2011 ).

Information and communication technology (ICT), such as web-based application and social networking sites enhances the collaboration and construction of knowledge byway of instruction with outside experts (Zhu, 2012 ). A positive statistically significant relationship was found between student’s use of a variety of social media tools and the colleague’s fellow as well as the overall quality of experiences (Rutherford, 2010 ). The potential use of social media leads to collaborative learning environments which allow students to share education-related materials and contents (Fisher & Baird, 2006 ). The report of 233 students in the United States higher educations confirmed that more recluse students interact through social media, which assist them in collaborative learning and boosting their self-confidence (Voorn & Kommers, 2013 ). Thus hypotheses as

H2: Use of social media for collaborative learning is positively associated with interactivity with peers.

Social media for collaborative learning, interactivity with peers, online knowledge sharing behaviour and students’ engagement

Students’ engagement in social media and its types represent their physical and mental involvement and time spent boost to the enhancement of educational Excellency, time spent on interaction with peers, teachers for collaborative learning (Kuh, 2007 ). Students’ engagement enhanced when interacting with peers and teacher was in the same direction, shares of ideas (Chickering & Gamson, 1987 ). Engagement is an active state that is influenced by interaction or lack thereof (Leece, 2011 ). With the advancement in information technology, the virtual world becomes the storehouse of the information. Liccardi et al. ( 2007 ) concluded that 30% students were noted to be active on social media for interaction with their colleagues, tutors, and friends while more than 52% used some social media forms for video sharing, blogs, chatting, and wiki during their class time. E-learning becomes now sharp and powerful tools in information technology and makes a substantial impact on the student’s academic performance. Sharing your knowledge will make you better. Social network ties were shown to be the best predictors of online knowledge sharing intention, which in turn associated with knowledge sharing behaviour (Chen, Chen, & Kinshuk, 2009 ). Social media provides the robust personalised, interactive learning environment and enhances in self-motivation as cited in (Al-Mukhaini, Al-Qayoudhi, & Al-Badi, 2014 ). Therefore, it was hypothesised that:

H3: Use of social media for collaborative learning is positively associated with online knowledge sharing behaviour.

Broadly Speaking social media/sites allow the students to interact, share the contents with colleagues, also assisting in building connections with others (Cain, 2008 ). In the present era, the majority of the college-going students are seen to be frequent users of these sophisticated devices to keep them informed and updated about the external affair. Facebook reported per day 1,00,000 new members join; Facebook is the most preferred social networking sites among the students of the United States as cited in (Cain, 2008 ). The researcher of the school of engineering, Swiss Federal Institute of Technology Lausanne, Switzerland, designed and developed Grasp, a social media platform for their students’ collaborative learning, sharing contents (Bogdanov et al., 2012 ). The utility and its usefulness could be seen in the University of Geneva and Tongji University at both two educational places students were satisfied and accept ‘ Grasp’ to collect, organised and share the contents. Students use of social media will interact ubiquity, heterogeneous and engaged in large groups (Wankel, 2009 ). So we hypotheses

H4: More interaction with teachers leads to higher students’ engagement.

However, a similar report published on 233 students revealed that social media assisted in their collaborative learning and self-confidence as they prefer communication technology than face to face communication. Although, the students have the willingness to communicate via social media platform than face to face (Voorn & Kommers, 2013 ). The potential use of social media tools facilitates in achieving higher-level learning through collaboration with colleagues and other renewed experts in their field (Junco, Heiberger, & Loken, 2011 ; Meyer, 2010 ; Novak, Razzouk, & Johnson, 2012 ; Redecker, Ala-Mutka, & Punie, 2010 ). Academic self-efficacy and optimism were found to be strongly related to performance, adjustment and consequently both directly impacted on student’s academic performance (Chemers, Hu, & Garcia, 2001 ). Data of 723 Malaysian researchers confirmed that both male and female students were satisfied with the use of social media for collaborative learning and engagement was found positively affected with learning performance (Al-Rahmi, Alias, Othman, Marin, & Tur, 2018 ). Social media were seen as a powerful driver for learning activities in terms of frankness, interactivity, and friendliness.

Junco et al. ( 2011 ) conducted research on the specific purpose of the social media; how Twitter impacted students’ engagement, found that it was extent discussion out of class, their participation in panel group (Rodriguez, 2011 ). A comparative study conducted by (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) revealed that students were more techno-oriented than faculty members and more likely using Facebook and such similar communication technology to support their class-related task. Additionally, faculty members were more likely to use traditional techniques, i.e. email. Thus hypotheses framed is that:

H5: More interaction with peers ultimately leads to better students’ engagement.

Social networking sites and social media are closely similar, which provide a platform where students can interact, communicate, and share emotional intelligence and looking for people with other attitudes (Gikas & Grant, 2013 ). Facebook and YouTube channel use also increased in the skills/ability and knowledge and outcomes (Daniel, Isaac, & Janet, 2017 ). It was highlighted that 90% of faculty members were using some sort of social media in their courses/ teaching. Facebook was the most visited social media sites as per study, 40% of faculty members requested students to read and views content posted on social media; majority reports that videos, wiki, etc. the primary source of acquiring knowledge, social networking sites valuable tool/source of collaborative learning (Moran et al., 2011 ). However, more interestingly, in a study which was carried out on 658 faculty members in the eight different state university of Turkey, concluded that nearly half of the faculty member has some social media accounts.

Further reported that adopting social media for educational purposes, the primary motivational factor which stimulates them to use was effective and quick means of communication technology (Akçayır, 2017 ). Thus hypotheses formulated is:

H6: Online knowledge sharing behaviour is positively associated with the students’ engagement.

Using multiple treatment research design, following act-react to increase students’ academic performance and productivity, it was observed when self–monitoring record sheet was placed before students and seen that students engagement and educational productivity was increased (Rock & Thead, 2007 ). Student engagement in extra curriculum activities promotes academic achievement (Skinner & Belmont, 1993 ), increases grade rate (Connell, Spencer, & Aber, 1994 ), triggering student performance and positive expectations about academic abilities (Skinner & Belmont, 1993 ). They are spending time on online social networking sites linked to students engagement, which works as the motivator of academic performance (Fan & Williams, 2010 ). Moreover, it was noted in a survey of over 236 Malaysian students that weak association found between the online game and student’s academic performance (Eow, Ali, Mahmud, & Baki, 2009 ). In a survey of 671 students in Jordan, it was revealed that student’s engagement directly influences academic performance, also seen the indirect effect of parental involvement over academic performance (Al-Alwan, 2014 ). Engaged students are perceptive and highly active in classroom activities, ready to participate in different classroom extra activities and expose motivation to learn, which finally leads in academic achievement (Reyes, Brackett, Rivers, White, & Salovey, 2012 ). A mediated role of students engagement seen in 1399 students’ classroom emotional climate and grades (Reyes et al., 2012 ). A statistically significant relation was noticed between online lecture and exam performance.

Nonetheless, intelligence quotient, personality factors, students must be engaged in learning activities as cited in (Bertheussen & Myrland, 2016 ). The report of the 1906 students at 7 universities in Colombia confirmed that the weak correlation between collaborative learning, students faculty interaction with academic performance (Pineda-Báez et al., 2014 ) Thus, the hypothesis

H7: Student's Engagement is positively associated with the student's academic performance.

Methodology

To check the students’ perception on social media for collaborative learning in higher education institutions, Data were gathered both offline and online survey administered to students from one public university in Eastern India (BBAU, Lucknow). For the sake of this study, indicators of interactivity with peers and teachers, the items of students engagement, the statement of social media for collaborative learning, and the elements of students’ academic performance were adopted from (AL-Rahmi & Othman, 2013 ). The statement of online knowledge sharing behaviour was taken from (Ma & Yuen, 2011 ).

The indicators of all variables which were mentioned above are measured on the standardised seven-point Likert scale with the anchor (1-Strongly Disagree, to 7-Strongly Agree). Interactivity with peers was measured using four indicators; the sample items using social media in class facilitates interaction with peers ; interactivity with teachers was measured using four symbols, the sample item is using social media in class allows me to discuss with the teacher. ; engagement was measured using three indicators by using social media I felt that my opinions had been taken into account in this class ; social media for collaborative learning was measured using four indicators collaborative learning experience in social media environment is better than in a face-to-face learning environment ; students’ academic performance was measured using five signs using social media to build a student-lecturer relationship with my lecturers, and this improves my academic performance ; online knowledge sharing behaviour was assessed using five symbols the counsel was received from other colleague using social media has increased our experience .

Procedure and measurement

A sample of 360 undergraduate students was collected by convenience sampling method of a public university in Eastern India. The proposed model of study was measured and evaluated using variance based structured equation model (SEM)-a latent multi variance technique which provides the concurrent estimation of structural and measurement model that does not meet parametric assumption (Coelho & Duarte, 2016 ; Haryono & Wardoyo, 2012 ; Lee, 2007 ; Moqbel, Nevo, & Kock, 2013 ; Raykov & Marcoulides, 2000 ; Williams, Rana, & Dwivedi, 2015 ). The confirmatory factor analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminate and convergent validity met or not. The loading of all the indicators should be 0.50 or more (Field, 2011 ; Hair, Anderson, Tatham, & Black, 1992 ). And it should be statistically significant at least at the 0.05.

Demographic analysis (Table 1 )

The majority of the students in this study were females (50.8%) while male students were only 49.2% with age 15–20 years (71.7%). It could be pointed out at this juncture that the majority of the students (53.9%) in BBAU were joined at least 1–5 academic pages for their getting information, awareness and knowledge. 46.1% of students spent 1–5 h per week on social networking sites for collaborative learning, interaction with teachers at an international level. The different academic pages followed for accessing material, communication with the faculty members stood at 44.4%, there would be various forms of the social networking sites (LinkedIn, Slide Share, YouTube Channel, Researchgate) which provide the facility of online collaborative learning, a platform at which both faculty members and students engaged in learning activities.

As per report (Nasir, Khatoon, & Bharadwaj, 2018 ), most of the social media user in India are college-going students, 33% girls followed by 27% boys students, and this reports also forecasted that India is going to become the highest 370.77 million internet users in 2022. Additionally, the majority of the faculty members use smartphone 44% to connect with the students for sharing material content. Technological advantages were the pivotal motivational force which stimulates faculty members and students to exploits the opportunities of resource materials (Nasir & Khan, 2018 ) (Fig. 2 ).

Reasons for Using Social Media

When the students were asked for what reason did they use social media, it was seen that rarely using for self-promotion, very frequently using for self-education, often used for passing the time with friends, and so many fruitful information the image mentioned above depicting.

Instrument validation

The structural model was applied to scrutinize the potency and statistically significant relationship among unobserved variables. The present measurement model was evaluated using Confirmatory Factor Analysis (CFA), and allied procedures to examine the relationship among hypothetical latent variables has acceptable reliability and validity. This study used both SPSS 20.0 and AMOS to check measurement and structural model (Field, 2013 ; Hair, Anderson, et al., 1992 ; Mooi & Sarstedt, 2011 ; Norusis, 2011 ).

The Confirmatory Factor Analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminant and convergent validity met or not. The loading of all the indicators should be 0.70 or more it should be statistically significant at least at the 0.05 (Field, 2011 ; Hair, Anderson, et al., 1992 ).

CR or CA-based tests measured the reliability of the proposed measurement model. The CA provides an estimate of the indicators intercorrelation (Henseler & Sarstedt, 2013 . The benchmark limits of the CA is 0.7 or more (Nunnally & Bernstein, 1994 ). As per Table 2 , all latent variables in this study above the recommended threshold limit. Although, Average Variance Extracted (AVE) has also been demonstrated which exceed the benchmark limit 0.5. Thus all the above-specified values revealed that our instrument is valid and effective. (See Table 2 for the additional information) (Table 3 ).

In a nutshell, the measurement model clear numerous stringent tests of convergent validity, discriminant validity, reliability, and absence of multi-collinearity. The finding demonstrated that our model meets widely accepted data validation criteria. (Schumacker & Lomax, 2010 ).

The model fit was evaluated through the Chi-Square/degree of freedom (CMIN/DF), Root Mean Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Goodness of fit index (GFI) and Tucker-Lewis Index (TLI). The benchmark limit of the CFI, TLI, and GFI 0.90or more (Hair et al., 2016 ; Kock, 2011 ). The model study demonstrated in the table, as mentioned above 4 that the minimum threshold limit was achieved (See Table 4 for additional diagnosis).

Path coefficient of several hypotheses has been demonstrated in Fig.  3 , which is a variable par relationship. β (beta) Coefficients, standardised partial regression coefficients signify the powers of the multivariate relationship among latent variables in the model. Remarkably, it was observed that seven out of the seven proposed hypotheses were accepted and 78% of the explained variance in students’ academic performance, 60% explained variance in interactivity with teachers, 48% variance in interactivity with peers, 43% variance in online knowledge sharing behaviour and 79% variance in students’ engagement. Social media collaborative learning has a significant association with teacher interactivity(β = .693, P  < 0.001), demonstrating that there is a direct effect on interaction with the teacher by social media when other variables are controlled. On the other hand, use of social media for collaborative learning has noticed statistically significant positive relationship with peers interactivity (β = .704, p  < 0.001) meaning thereby, collaborative learning on social media by university students, leads to the high degree of interaction with peers, colleagues. Implied 10% rise in social media use for learning purposes, expected 7.04% increase in interaction with peers.

Path Diagram

Use of social media for collaborating learning has a significant positive association with online knowledge sharing behaviour (β = .583, p  < 0.001), meaning thereby that the more intense use of social media for collaborative learning by university students, the more knowledge sharing between peers and colleagues. Also, interaction with the teacher seen the significant statistical positive association with students engagement (β = .450, p  < 0.001), telling that the more conversation with teachers, leads to a high level of students engagement. Similarly, the practical interpretation of this result is that there is an expected 4.5% increase in student’s participation for every 10% increase in interaction with teachers. Interaction with peers has a significant positive association with students engagement (β = .210, p  < 0.001). Practically, the finding revealed that 10% upturn in student’s involvement, there is a 2.1% increase in peer’s interaction. There is a significant positive association between online knowledge sharing behaviour and students engagement (β = 0.247, p  < 0.001), and finally students engagement has been a statistically significant positive relationship with students’ academic performance (β = .972, p  < 0.001), this is the clear indication that more engaged students in collaborative learning via social media leads to better students’ academic performance.

Discussion and implication

There is a continuing discussion in the academic literature that use of such social media and social networking sites would facilitate collaborative learning. It is human psychology generally that such communication media technology seems only for entertainment, but it should be noted here carefully that if such communication technology would be followed with due attention prove productive. It is essential to acknowledge that most university students nowadays adopting social media communication to interact with colleagues, teachers and also making the group be in touch with old friends and even a convenient source of transferring the resources. In the present era, the majority of the university students having diversified social media community groups like Whatsapp, Facebook pages following different academic web pages to upgrade their knowledge.

Practically for every 10% rise in students’ engagement, expected to be 2.1% increase in peer interaction. As the study suggested that students engage in different sites, they start discussing with colleagues. More engaged students in collaborative learning through social media lead better students’ academic performance. The present study revealed that for every 10% increase in student’s engagement, there would be an expected increase in student academic performance at a rate of 9.72. This extensive research finding revealed that the application of online social media would facilitate the students to become more creative, dynamics and connect to the worldwide instructor for collaborative learning.

Accordingly, the use of online social media for collaborative learning, interaction with mentors and colleagues leadbetter student’s engagement which consequently affects student’s academic performance. The higher education authority should provide such a platform which can nurture the student’s intellectual talents. Based on the empirical investigation, it would be said that students’ engagement, social media communication devices facilitate students to retrieve information and interact with others in real-time regarding sharing teaching materials contents. Additionally, such sophisticated communication devices would prove to be more useful to those students who feel too shy in front of peers; teachers may open up on the web for the collaborative learning and teaching in the global scenario and also beneficial for physically challenged students. It would also make sense that intensive use of such sophisticated technology in teaching pedagogical in higher education further facilitates the teachers and students to interact digitally, web-based learning, creating discussion group, etc. The result of this investigation confirmed that use of social media for collaborative learning purposes, interaction with peers, and teacher affect their academic performance positively, meaning at this moment that implementation of such sophisticated communication technology would bring revolutionary, drastic changes in higher education for international collaborative learning (Table 5 ).

Limitations and future direction

Like all the studies, this study is also not exempted from the pitfalls, lacunas, and drawbacks. The first and foremost research limitation is it ignores the addiction of social media; excess use may lead to destruction, deviation from the focal point. The study only confined to only one academic institution. Hence, the finding of the project cannot be generalised as a whole. The significant positive results were found in this study due to the fact that the social media and mobile devices are frequently used by the university going students not only as a means of gratification but also for educational purposes.

Secondly, this study was conducted on university students, ignoring the faculty members, it might be possible that the faculty members would not have been interested in interacting with the students. Thus, future research could be possible towards faculty members in different higher education institutions. To the authors’ best reliance, this is the first and prime study to check the usefulness and applicability of social media in the higher education system in the Indian context.

Concluding observations

Based on the empirical investigation, it could be noted that application and usefulness of the social media in transferring the resource materials, collaborative learning and interaction with the colleagues as well as teachers would facilitate students to be more enthusiastic and dynamic. This study provides guidelines to the corporate world in formulating strategies regarding the use of social media for collaborative learning.

Availability of data and materials

The corresponding author declared here all types of data used in this study available for any clarification. The author of this manuscript ready for any justification regarding the data set. To make publically available of the data used in this study, the seeker must mail to the mentioned email address. The profile of the respondents was completely confidential.

Akçayır, G. (2017). Why do faculty members use or not use social networking sites for education? Computers in Human Behavior, 71 , 378–385.

Article   Google Scholar  

Al-Alwan, A. F. (2014). Modeling the relations among parental involvement, school engagement and academic performance of high school students. International Education Studies, 7 (4), 47–56.

Al-Mukhaini, E. M., Al-Qayoudhi, W. S., & Al-Badi, A. H. (2014). Adoption of social networking in education: A study of the use of social networks by higher education students in Oman. Journal of International Education Research, 10 (2), 143–154.

Google Scholar  

Al-Rahmi, W. M., Alias, N., Othman, M. S., Marin, V. I., & Tur, G. (2018). A model of factors affecting learning performance through the use of social media in Malaysian higher education. Computers & Education, 121 , 59–72.

Al-Rahmi, W. M., & Othman, M. S. (2013). Evaluating student’s satisfaction of using social media through collaborative learning in higher education. International Journal of Advances in Engineering & Technology, 6 (4), 1541–1551.

Arbuckle, J. (2008). Amos 17.0 user's guide . Chicago: SPSS Inc..

Barnes, N. G., & Lescault, A. M. (2011). Social media adoption soars as higher-ed experiments and reevaluates its use of new communications tools . North Dartmouth: Center for Marketing Research. University of Massachusetts Dartmouth.

Bertheussen, B. A., & Myrland, Ø. (2016). Relation between academic performance and students’ engagement in digital learning activities. Journal of Education for Business, 91 (3), 125–131.

Bogdanov, E., Limpens, F., Li, N., El Helou, S., Salzmann, C., & Gillet, D. (2012). A social media platform in higher education. In Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–8). IEEE.

Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/windows: basic concepts, applications, and programming . Thousand Oaks: Sage.

Cain, J. (2008). Online social networking issues within academia and pharmacy education. American Journal of Pharmaceutical Education. https://doi.org/10.5688/aj720110 .

Cao, Y., Ajjan, H., & Hong, P. (2013). Using social media applications for educational outcomes in college teaching: a structural equation analysis. British Journal of Educational Technology, 44 (4), 581–593. https://doi.org/10.1111/bjet.12066 .

Cavus, N., & Ibrahim, D. (2008). A mobile tool for learning English words, Online Submission (pp. 6–9) Retrieved from http://libezproxy.open.ac.uk/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED504283&site=ehost-live&scope=site .

Cavus, N., & Ibrahim, D. (2009). M-learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology, 40 (1), 78–91.

Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first-year college student performance and adjustment. Journal of Educational Psychology, 93 (1), 55–64. https://doi.org/10.1037/0022-0663.93.1.55 .

Chen, I. Y. L., Chen, N.-S., & Kinshuk. (2009). International forum of Educational Technology & Society Examining the factors influencing participants’ knowledge sharing behavior in virtual learning communities published by : International forum of Educational Technology & Society Examining the factor. Educational Technology & Society, 12 (1), 134–148.

Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practise in undergraduate education. AAHE bulletin, 3 , 7.

Coelho, J., & Duarte, C. (2016). A literature survey on older adults' use of social network services and social applications. Computers in Human Behavior, 58 , 187–205.

Connell, J. P., Spencer, M. B., & Aber, J. L. (1994). Educational risk and resilience in African-American youth: Context, self, action, and outcomes in school. Child Development, 65 (2), 493–506.

Daniel, E. A., Isaac, E. N., & Janet, A. K. (2017). Influence of Facebook usage on employee productivity: A case of university of cape coast staff. African Journal of Business Management, 11 (6), 110–116. https://doi.org/10.5897/AJBM2017.8265 .

DeBell, M., & Chapman, C. (2006). Computer and internet use by students in 2003. Statistical analysis report. NCES 2006-065. National Center for education statistics.

Dziuban, C., & Walker, J. D. (2012). ECAR Study of Undergraduate Students and Information Technology, 2012 (Research Report) . Louisville: EDUCAUSE Centre for Applied Research.

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99 , 14–27. https://doi.org/10.1016/j.compedu.2016.04.007 .

Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6 (3), 192.

Eow, Y. L., Ali, W. Z. b. W., Mahmud, R. b., & Baki, R. (2009). Form one students’ engagement with computer games and its effect on their academic achievement in a Malaysian secondary school. Computers and Education, 53 (4), 1082–1091. https://doi.org/10.1016/j.compedu.2009.05.013 .

Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Wiley Online Library, 45 (5), 902–915. https://doi.org/10.1111/bjet.12099 .

Fan, W., & Williams, C. M. (2010). The effects of parental involvement on students’ academic self-efficacy, engagement and intrinsic motivation. Educational Psychology, 30 (1), 53–74. https://doi.org/10.1080/01443410903353302 .

Field, A. (2011). Discovering statistics using SPSS: (and sex and drugs and rock'n'roll) (Vol. 497). London: Sage.

Field, A. (2013). Factor analysis using SPSS. Scientific Research and Essays, 22 (June), 1–26. https://doi.org/10.1016/B978-0-444-52272-6.00519-5 .

Fisher, M., & Baird, D. E. (2006). Making mLearning work: Utilizing mobile technology for active exploration, collaboration, assessment, and reflection in higher education. Journal of Educational Technology Systems, 35 (1), 3–30.

Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones &amp; social media. Internet and Higher Education Mobile, 19 , 18–26. https://doi.org/10.1016/j.iheduc.2013.06.002 .

Greenhow, C. (2011a). Online social networks and learning. On the horizon, 19 (1), 4–12.

Greenhow, C. (2011b). Youth, learning, and social media. Journal of Educational Computing Research, 45 (2), 139–146. https://doi.org/10.2190/EC.45.2.a .

Hair Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis. International Journal of Pharmaceutics . https://doi.org/10.1016/j.ijpharm.2011.02.019 .

Hair Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review.

Harrington, D. (2009). Confirmatory factor analysis . Oxford university press.

Haryono, S., & Wardoyo, P. (2012). Structural Equation Modeling (Vol. 331).

Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28 (2), 565–580.

Jackson, C. (2011). Your students love social media… and so can you. Teaching Tolerance, 39 , 38–41.

Junco, R., Heiberger, G., & Loken, E. (2011). The effect of twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27 (2), 119–132.

Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learning of English in institutions of higher education? The Internet and Higher Education, 13 (4), 179–187.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1), 59–68.

Kock, N. (2011). Using WarpPLS in e-collaboration studies: Mediating effects, control and second order variables, and algorithm choices. International Journal of e-Collaboration (IJeC), 7 (3), 1–13.

Kuh, G. D. (2007). What student engagement data tell us about college readiness. Peer Review, 9 (1), 4–8.

Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20 (3), 271–289.

Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach (Wiley series in probability and statistics). Ecotoxicology and Environmental Safety, 73 . https://doi.org/10.1016/j.ecoenv.2009.09.012 .

Leece, R. (2011). Engaging students through social media. Journal of the Australian and New Zealand Student Services Association, 38 , 10–14 Retrieved from https://www.researchgate.net/profile/Anthony_Jorm/publication/235003484_Introduction_to_guidelines_for_tertiary_education_institutions_to_assist_them_in_supporting_students_with_mental_health_problems/links/0c96052ba5314e1202000000.pdf#page=67 .

Lenhart, A., Arafeh, S., & Smith, A. (2008). Writing, technology and teens . Pew Internet & American Life Project.

Lenhart, A., Madden, M., & Hitlin, P. (2005). Teens and technology (p. 2008). Washington, DC: Pew Charitable Trusts Retrieved September 29.

Liccardi, I., Ounnas, A., Pau, R., Massey, E., Kinnunen, P., Lewthwaite, S., …, Sarkar, C. (2007). The role of social networks in students’ learning experiences. In ACM Sigcse Bulletin (39, 4, 224–237).

Ma, W. W. K., & Yuen, A. H. K. (2011). Understanding online knowledge sharing: An interpersonal relationship perspective. Computers & Education, 56 (1), 210–219.

Madden, M., & Zickuhr, K. (2011). 65% of online adults use social networking sites. Pew Internet & American Life Project, 1 , 14.

Meyer, K. A. (2010). A comparison of web 2.0 tools in a doctoral course. The Internet and Higher Education, 13 (4), 226–232.

Mirela Mabić, D. G. (2014). Facebook as a learning tool. Igarss, 2014 (1), 1–5. https://doi.org/10.1007/s13398-014-0173-7.2 .

Mooi, E., & Sarstedt, M. (2011). A concise guide to market research: The process, data, and methods using IBM SPSS statistics . Springeringer. https://doi.org/10.1007/978-3-642-12541-6 .

Moqbel, M., Nevo, S., & Kock, N. (2013). Organizational members’ use of social networking sites and job performance. Information Technology & People, 26 (3), 240–264. https://doi.org/10.1108/ITP-10-2012-0110 .

Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning, and sharing: How Today’s higher education faculty use social media (pp. 1–16). Babson survey research group, (April. https://doi.org/10.1016/j.chb.2013.06.015 .

Nasir, J. A., & Khan, N. A. (2018). Faculty member usage of social media and mobile devices in higher education institution. International Journal of Advance and Innovative Research, 6 (1), 17–25.

Nasir, J. A., Khatoon, A., & Bharadwaj, S. (2018). Social media users in India: A futuristic approach. International Journal of Research and Analytical Reviews, 5 (4), 762–765 Retrieved from http://ijrar.com/ .

Nihalani, P. K., & Mayrath, M. C. (2010). Statistics I. Findings from using an iPhone app in a higher education course. In White Paper .

Norusis, M. (2011). IBM SPSS statistics 20 brief guide (pp. 1–170). IBM Corporation Retrieved from http://www.ibm.com/support .

Novak, E., Razzouk, R., & Johnson, T. E. (2012). The educational use of social annotation tools in higher education: A literature review. The Internet and Higher Education, 15 (1), 39–49.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory .

Pineda-Báez, C., José-Javier, B. A., Rubiano-Bello, Á., Pava-García, N., Suárez-García, R., & Cruz-Becerra, F. (2014). Student engagement and academic performance in the Colombian University context. RELIEVE-Revista Electrónica de Investigación y Evaluación Educativa, 20 (2), 1–19.

Raykov, T., & Marcoulides, G. A. (2000). A First Course in Structural Equation Modeling .

Redecker, C., Ala-Mutka, K., & Punie, Y. (2010). Learning 2.0-the impact of social media on learning in Europe. Policy brief. JRC scientific and technical report. EUR JRC56958 EN, Available from http://bit.ly/cljlpq . Accessed 6 Feb 2011.

Reuben, B. R. (2008). The use of social Media in Higher Education for marketing and communications : A guide for professionals in higher education (Vol. 5) Retrieved from httpdoteduguru comwpcontentuploads200808socialmediainhighereducation pdf)). https://doi.org/10.1108/S2044-9968(2012)0000005018 .

Book   Google Scholar  

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104 (3), 700–712. https://doi.org/10.1037/a0027268 .

Richardson, J., & Lenarcic, J. (2008). Text Messaging as a Catalyst for Mobile Student Administration: The “Trigger” Experience. International Journal of Emerging Technologies & Society, 6 (2), 140–155.

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13 (3), 134–140.

Rock, M. L., & Thead, B. K. (2007). The effects of fading a strategic self-monitoring intervention on students’ academic engagement, accuracy, and productivity. Journal of Behavioral Education, 16 (4), 389–412. https://doi.org/10.1007/s10864-007-9049-7 .

Rodriguez, J. E. (2011). Social media use in higher education : Key areas to consider for educators. MERLOT Journal of Online Learning and Teaching, 7 (4), 539–550 https://doi.org/ISSN1558-9528 .

Rutherford, C. (2010). Using online social media to support Preservice student engagement. MERLOT Journal of Online Learning and Teaching, 6 (4), 703–711 Retrieved from http://jolt.merlot.org/vol6no4/rutherford_1210.pdf .

Schumacker, R. E., & Lomax, R. G. (2010). A Beginner’s Guide to structural equation modeling (3rd ed.). New York: Taylor & Francis Group.

Selwyn, N. (2012). Making sense of young people, education and digital technology: The role of sociological theory. Oxford Review of Education, 38 (1), 81–96.

Shih, Y. E. (2007). Setting the new standard with mobile computing in online learning. The International Review of Research in Open and Distributed Learning, 8 (2), 1–16.

Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of educational psychology, 85 (4), 571.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston: Pearson.

Voorn, R. J., & Kommers, P. A. (2013). Social media and higher education: Introversion and collaborative learning from the student’s perspective. International Journal of Social Media and Interactive Learning Environments, 1 (1), 59–73.

Wankel, C. (2009). Management education using social media. Organization Management Journal, 6 (4), 251–262.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28 (3), 443–488.

Zhu, C. (2012). Student satisfaction, performance, and knowledge construction in online collaborative learning. Journal of Educational Technology & Society, 15 (1), 127–136.

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Jamal Abdul Nasir Ansari & Nawab Ali Khan

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Jamal Abdul Nasir Ansari: The first author of this manuscript has performed all sorts of necessary works like the collection of data from respondents, administration of the questionnaire. Collection of information from the respondents was quite challenging. The author faced a lot of difficulties while collecting data. The main contribution of the author in this manuscript is that the entire work, like data analysis and its interpretation performed by him. Additionally, the author has tried to explore and usefulness of social media and its applicability in transferring the course contents. Nawab Ali Khan: The second author of this manuscript has checked all types of grammatical issues, and necessary corrections wherever required. The author(s) read and approved the final manuscript.

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Ansari, J.A.N., Khan, N.A. Exploring the role of social media in collaborative learning the new domain of learning. Smart Learn. Environ. 7 , 9 (2020). https://doi.org/10.1186/s40561-020-00118-7

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Received : 27 November 2019

Accepted : 18 February 2020

Published : 16 March 2020

DOI : https://doi.org/10.1186/s40561-020-00118-7

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