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Social Media Use and Its Connection to Mental Health: A Systematic Review

Fazida karim.

1 Psychology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

2 Business & Management, University Sultan Zainal Abidin, Terengganu, MYS

Azeezat A Oyewande

3 Family Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

4 Family Medicine, Lagos State Health Service Commission/Alimosho General Hospital, Lagos, NGA

Lamis F Abdalla

5 Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

Reem Chaudhry Ehsanullah

Safeera khan.

Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for quality. Eight papers were cross-sectional studies, three were longitudinal studies, two were qualitative studies, and others were systematic reviews. Findings were classified into two outcomes of mental health: anxiety and depression. Social media activity such as time spent to have a positive effect on the mental health domain. However, due to the cross-sectional design and methodological limitations of sampling, there are considerable differences. The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies.

Introduction and background

Human beings are social creatures that require the companionship of others to make progress in life. Thus, being socially connected with other people can relieve stress, anxiety, and sadness, but lack of social connection can pose serious risks to mental health [ 1 ].

Social media

Social media has recently become part of people's daily activities; many of them spend hours each day on Messenger, Instagram, Facebook, and other popular social media. Thus, many researchers and scholars study the impact of social media and applications on various aspects of people’s lives [ 2 ]. Moreover, the number of social media users worldwide in 2019 is 3.484 billion, up 9% year-on-year [ 3 - 5 ]. A statistic in Figure  1  shows the gender distribution of social media audiences worldwide as of January 2020, sorted by platform. It was found that only 38% of Twitter users were male but 61% were using Snapchat. In contrast, females were more likely to use LinkedIn and Facebook. There is no denying that social media has now become an important part of many people's lives. Social media has many positive and enjoyable benefits, but it can also lead to mental health problems. Previous research found that age did not have an effect but gender did; females were much more likely to experience mental health than males [ 6 , 7 ].

Impact on mental health

Mental health is defined as a state of well-being in which people understand their abilities, solve everyday life problems, work well, and make a significant contribution to the lives of their communities [ 8 ]. There is debated presently going on regarding the benefits and negative impacts of social media on mental health [ 9 , 10 ]. Social networking is a crucial element in protecting our mental health. Both the quantity and quality of social relationships affect mental health, health behavior, physical health, and mortality risk [ 9 ]. The Displaced Behavior Theory may help explain why social media shows a connection with mental health. According to the theory, people who spend more time in sedentary behaviors such as social media use have less time for face-to-face social interaction, both of which have been proven to be protective against mental disorders [ 11 , 12 ]. On the other hand, social theories found how social media use affects mental health by influencing how people view, maintain, and interact with their social network [ 13 ]. A number of studies have been conducted on the impacts of social media, and it has been indicated that the prolonged use of social media platforms such as Facebook may be related to negative signs and symptoms of depression, anxiety, and stress [ 10 - 15 ]. Furthermore, social media can create a lot of pressure to create the stereotype that others want to see and also being as popular as others.

The need for a systematic review

Systematic studies can quantitatively and qualitatively identify, aggregate, and evaluate all accessible data to generate a warm and accurate response to the research questions involved [ 4 ]. In addition, many existing systematic studies related to mental health studies have been conducted worldwide. However, only a limited number of studies are integrated with social media and conducted in the context of social science because the available literature heavily focused on medical science [ 6 ]. Because social media is a relatively new phenomenon, the potential links between their use and mental health have not been widely investigated.

This paper attempt to systematically review all the relevant literature with the aim of filling the gap by examining social media impact on mental health, which is sedentary behavior, which, if in excess, raises the risk of health problems [ 7 , 9 , 12 ]. This study is important because it provides information on the extent of the focus of peer review literature, which can assist the researchers in delivering a prospect with the aim of understanding the future attention related to climate change strategies that require scholarly attention. This study is very useful because it provides information on the extent to which peer review literature can assist researchers in presenting prospects with a view to understanding future concerns related to mental health strategies that require scientific attention. The development of the current systematic review is based on the main research question: how does social media affect mental health?

Research strategy

The research was conducted to identify studies analyzing the role of social media on mental health. Google Scholar was used as our main database to find the relevant articles. Keywords that were used for the search were: (1) “social media”, (2) “mental health”, (3) “social media” AND “mental health”, (4) “social networking” AND “mental health”, and (5) “social networking” OR “social media” AND “mental health” (Table  1 ).

Out of the results in Table  1 , a total of 50 articles relevant to the research question were selected. After applying the inclusion and exclusion criteria, duplicate papers were removed, and, finally, a total of 28 articles were selected for review (Figure  2 ).

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Inclusion and exclusion criteria

Peer-reviewed, full-text research papers from the past five years were included in the review. All selected articles were in English language and any non-peer-reviewed and duplicate papers were excluded from finally selected articles.

Of the 16 selected research papers, there were a research focus on adults, gender, and preadolescents [ 10 - 19 ]. In the design, there were qualitative and quantitative studies [ 15 , 16 ]. There were three systematic reviews and one thematic analysis that explored the better or worse of using social media among adolescents [ 20 - 23 ]. In addition, eight were cross-sectional studies and only three were longitudinal studies [ 24 - 29 ].The meta-analyses included studies published beyond the last five years in this population. Table  2  presents a selection of studies from the review.

IGU, internet gaming disorder; PSMU, problematic social media use

This study has attempted to systematically analyze the existing literature on the effect of social media use on mental health. Although the results of the study were not completely consistent, this review found a general association between social media use and mental health issues. Although there is positive evidence for a link between social media and mental health, the opposite has been reported.

For example, a previous study found no relationship between the amount of time spent on social media and depression or between social media-related activities, such as the number of online friends and the number of “selfies”, and depression [ 29 ]. Similarly, Neira and Barber found that while higher investment in social media (e.g. active social media use) predicted adolescents’ depressive symptoms, no relationship was found between the frequency of social media use and depressed mood [ 28 ].

In the 16 studies, anxiety and depression were the most commonly measured outcome. The prominent risk factors for anxiety and depression emerging from this study comprised time spent, activity, and addiction to social media. In today's world, anxiety is one of the basic mental health problems. People liked and commented on their uploaded photos and videos. In today's age, everyone is immune to the social media context. Some teens experience anxiety from social media related to fear of loss, which causes teens to try to respond and check all their friends' messages and messages on a regular basis.

On the contrary, depression is one of the unintended significances of unnecessary use of social media. In detail, depression is limited not only to Facebooks but also to other social networking sites, which causes psychological problems. A new study found that individuals who are involved in social media, games, texts, mobile phones, etc. are more likely to experience depression.

The previous study found a 70% increase in self-reported depressive symptoms among the group using social media. The other social media influence that causes depression is sexual fun [ 12 ]. The intimacy fun happens when social media promotes putting on a facade that highlights the fun and excitement but does not tell us much about where we are struggling in our daily lives at a deeper level [ 28 ]. Another study revealed that depression and time spent on Facebook by adolescents are positively correlated [ 22 ]. More importantly, symptoms of major depression have been found among the individuals who spent most of their time in online activities and performing image management on social networking sites [ 14 ].

Another study assessed gender differences in associations between social media use and mental health. Females were found to be more addicted to social media as compared with males [ 26 ]. Passive activity in social media use such as reading posts is more strongly associated with depression than doing active use like making posts [ 23 ]. Other important findings of this review suggest that other factors such as interpersonal trust and family functioning may have a greater influence on the symptoms of depression than the frequency of social media use [ 28 , 29 ].

Limitation and suggestion

The limitations and suggestions were identified by the evidence involved in the study and review process. Previously, 7 of the 16 studies were cross-sectional and slightly failed to determine the causal relationship between the variables of interest. Given the evidence from cross-sectional studies, it is not possible to conclude that the use of social networks causes mental health problems. Only three longitudinal studies examined the causal relationship between social media and mental health, which is hard to examine if the mental health problem appeared more pronounced in those who use social media more compared with those who use it less or do not use at all [ 19 , 20 , 24 ]. Next, despite the fact that the proposed relationship between social media and mental health is complex, a few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are required to clarify the underlying factors that help examine why social media has a negative impact on some peoples’ mental health, whereas it has no or positive effect on others’ mental health.

Conclusions

Social media is a new study that is rapidly growing and gaining popularity. Thus, there are many unexplored and unexpected constructive answers associated with it. Lately, studies have found that using social media platforms can have a detrimental effect on the psychological health of its users. However, the extent to which the use of social media impacts the public is yet to be determined. This systematic review has found that social media envy can affect the level of anxiety and depression in individuals. In addition, other potential causes of anxiety and depression have been identified, which require further exploration.

The importance of such findings is to facilitate further research on social media and mental health. In addition, the information obtained from this study can be helpful not only to medical professionals but also to social science research. The findings of this study suggest that potential causal factors from social media can be considered when cooperating with patients who have been diagnosed with anxiety or depression. Also, if the results from this study were used to explore more relationships with another construct, this could potentially enhance the findings to reduce anxiety and depression rates and prevent suicide rates from occurring.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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

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Large-scale quantitative evidence of media impact on public opinion toward China

• Junming Huang   ORCID: orcid.org/0000-0002-2532-4090 1 ,
• Gavin G. Cook 1 &
• Yu Xie 1 , 2  

Humanities and Social Sciences Communications volume  8 , Article number:  181 ( 2021 ) Cite this article

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Do mass media influence people’s opinions of other countries? Using BERT, a deep neural network-based natural language processing model, this study analyzes a large corpus of 267,907 China-related articles published by The New York Times since 1970. The output from The New York Times is then compared to a longitudinal data set constructed from 101 cross-sectional surveys of the American public’s views on China, revealing that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. This result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence the public opinion of foreign countries.

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Introduction.

America and China are the world’s two largest economies, and they are currently locked in a tense rivalry. In a democratic system, public opinion shapes and constrains political action. How the American public views China thus affects relations between the two countries. Because few Americans have personally visited China, most Americans form their opinions of China and other foreign lands from media depictions. Our paper aims to explain how Americans form their attitudes on China with a case study of how The New York Times may shape public opinion. Our analysis is not causal, but it is informed by a causal understanding of how public opinion may flow from the media to the citizenry.

Scholars have adopted a number of wide-ranging and even contradictory approaches to explain the relationships between media and the American mind. One school of thought stresses that media exposure shapes public opinion (Baum and Potter, 2008 ; Iyengar and Kinder, 2010 ). Another set of approaches focuses on how the public might lead the media by analyzing how consumer demand shapes reporting. Newspapers may attract readers by biasing coverage of polarizing issues towards the ideological proclivities of their readership (Mullainathan and Shleifer, 2005 ), and with the advent of social media platforms such as Facebook and Twitter, traditional media are now more responsive to audience demand than ever before (Jacobs and Shapiro, 2011 ). On the other side of this equation, news consumers generally tend to seek out news sources with which they agree (Iyengar et al., 2008 ), and politically active individuals do so more proactively than the average person (Zaller, 1992 ).

Two other approaches address factors outside the media–public binary. The first, stresses the role of elites in opinion formation. While some, famously including Noam Chomsky, argue that news media are unwitting at best and at worst complicit “shills” of the American political establishment, political elites may affect public opinion directly by communicating with the public (Baum and Potter, 2008 ). Foreign elites may also influence American opinion because American reporters sometimes circumvent domestic sources and ask trusted foreign experts and officials for opinions (Hayes and Guardino, 2011 ). The second stresses how the macro-level phenomenon of public sentiment is shaped by micro-level and meso-level processes. An adult’s opinions on various topics emerge from their personal values, many of which are set during and around adolescence from factors outside of the realm of individual control (Hatemi and McDermott, 2016 ). Social networks may also affect attitude formation (Kertzer and Zeitzoff, 2017 ).

In light of these contradictory interpretations, it is difficult to be sure whether the media shape the attitudes of consumers or, on the other hand, whether consumers shape media (Baum and Potter, 2008 ). Moreover, most of the theories summarized above are tested on relatively small slices of data. In order to offer an alternative, “big data”-based contribution to this ongoing debate, this study compares how the public views China and how the news media report on China with large-scale data. Our data set, which straddles 50 years of newspaper reporting and survey data, is uniquely large and includes more than a quarter-million articles from The New York Times.

Most extant survey data indicate that Americans do not seem to like China very much (Xie and Jin, 2021 ). Many Americans are reported to harbor doubts about China’s record on human rights (Aldrich et al., 2015 ; Cao and Xu, 2015 ) and are anxious about China’s burgeoning economic, military, and strategic power (Gries and Crowson, 2010 ; Yang and Liu, 2012 ). They also think that the Chinese political system fails to serve the needs of the Chinese people (Aldrich et al., 2015 ). Most Americans, however, recognize a difference between the Chinese state, the Chinese people, and Chinese culture, and they view the latter two more favorably (Gries and Crowson, 2010 ). In Fiske’s Stereotype Content Model (Fiske et al., 2002 ), which expresses common stereotypes as a combination of “competence” and “warmth”, Asians belong to a set of “high-status, competitive out-groups” and rank high in competence but low in warmth (Lin et al., 2005 ).

The New York Times, which calls itself the “Newspaper of Record”, is the most influential newspaper in the USA and possibly even in the Anglophonic world. It boasts 7.5 million subscribers (Business Wire, 2021 ), and while the paper’s reach may be impressive, it is yet more significant that the readership of The New York Times represents an elite subset of the American public. Print subscribers to The New York Times have a median household income of $191,000, three times the median income of US households writ large (Rothbaum and Edwards, 2019 ). Despite the paper’s haughty and sometimes condescending reporting, it “has had and still has immense social, political, and economic influence on American and the world” (Schwarz, 2012 , p. 81). The New York Times may be a paper for America’s elite, and it may be biased to reflect the tastes of its elite audience, but the paper’s ideological slant does not affect our analyses as long as the its relevant biases are consistent over the time period covered by our analyses. Our analyses support the intuition of qualitative work on The Times (Schwarz, 2012 ) and show that these biases remain more or less constant for the decades in our sample. These analyses also illuminate some of the paper’s more notable biases, including the paper’s particular predilection for globalization.

The impact of social media on traditional media is not straightforward. While new media have certainly changed old media, neither has replaced the other. It is more accurate to say that old media have been integrated into new media and, in some ways, become a form of new media themselves. Twitter has accelerated the 2000s-era trends of information access that made it possible for news readers to find their own news and also enabled readers to interact with journalists (Jacobs and Shapiro, 2011 ), and the The New York Times seems to have made a significant commitment to the Twitter ecosystem. A quick glance at the follower count of The Times’ official Twitter account shows that it is one of the most influential accounts on the site, with almost 50 million followers. For comparison, both current president Joe Biden and vice president Kamala Harris have around 10 million followers. Most New York Times reporters additionally have “verified” accounts on the platform, which means that individual reporters may be incentivized to maintain public-facing profiles more now than in the past.

The media consumption patterns that made new media possible have changed the way The New York Times interacts with its audience and how it extracts revenue. The New York Times boasts a grand total of 7.5 million subscribers, but only 800,000 of them subscribe to the print edition. The Times’ digital subscription base has boomed since the election of Donald J. Trump, growing almost sixfold from a paltry 1.3 million in 2015 to a staggering 6.7 million in 2020 (Business Wire, 2021 ). The Times increasingly relies more on digital subscriptions and less on print subscriptions and ad sales for revenue (Lee, 2020 ). Ad revenue for most papers has been in sharp decline since the early 2000s (Jacobs and Shapiro, 2011 ), and this trend has only continued into the present. The New York Times now operates almost like a direct-to-consumer, subscription tech startup. New media have not replaced but have certainly changed old media. The full impact of these changes is beyond the scope of this paper, and we suggest it as an area for further research.

A small body of prior work has studied the The New York Times and how The New York Times reports on China. Blood and Phillips use autoregression methods on time series data to predict public opinion (Blood and Phillips, 1995 ). Wu et al. use a similar autoregression technique and find that public sentiment regarding the economy predicts economic performance and that people pay more attention to economic news during recessions (Wu et al., 2002 ). Peng finds that coverage of China in the paper has been consistently negative but increasingly frequent as China became an economic powerhouse (Peng, 2004 ). There is very little other scholarship that applies language processing methods to large corpora of articles from The New York Times or other leading papers. Atalay et al. is an exception that uses statistical techniques for parsing natural languages to analyze a corpus of newspaper articles from The New York Times, the Wall Street Journal, and other leading papers in order to investigate the increasing use of information technologies in newspaper classifieds (Atalay et al., 2018 ).

We explore the impact of The New York Times on its readers by examining the general relationship between The Times and public opinion. Though some might contend that only elites read NYT, we have adopted this research strategy for two reasons. If the views of NYT only impacted the nation’s elite, the paper’s views would still propagate to the general public through the elites themselves because elites can affect public opinion outside of media channels (Baum and Potter, 2008 ). Additionally, it is a widely held belief that NYT serves as a general barometer of an agenda-setting agent for American culture (Schwarz, 2012 ). Because of these two reasons, we interpolate the relationship between NYT and public opinion from the relationship between NYT and its readers, and we extrapolate that the views of NYT are broadly representative of American media.

Our paper aims to advance understanding of how Americans form their attitudes on China with a case study of how The New York Times may shape public opinion. We hypothesize that media coverage of foreign nations affects how Americans view the rest of the world. This reduced-form model deliberately simplifies the interactions between audience and media and sidesteps many active debates in political psychology and political communication. Analyzing a corpus of 267,907 articles on China from The New York Times, we quantify media sentiment with BERT, a state-of-the-art natural language processing model with deep neural networks, and segment sentiment into eight domain topics. We then use conventional statistical methods to link media sentiment to a longitudinal data set constructed from 101 cross-sectional surveys of the American public’s views on China. We find strong correlations between how The New York Times reports on China in one year and the views of the public on China in the next. The correlations agree with our hypothesis and imply a strong connection between media sentiment and public opinion.

We quantify media sentiment with a natural language model on a large-scale corpus of 267,907 articles on China from The New York Times published between 1970 and 2019. To explore sentiment from this corpus in greater detail, we map every article to a sentiment category (positive, negative, or neutral) in eight topics: ideology, government and administration, democracy, economic development, marketization, welfare and well-being, globalization, and culture.

We do this with a three-stage modeling procedure. First, two human coders annotate 873 randomly selected articles with a total of 18,598 paragraphs expressing either positive, negative, or neutral sentiment in each topic. We treat irrelevant articles as neutral sentiments. Secondly, we fine-tune a natural language processing model Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018 ) with the human-coded labels. The model uses a deep neural network with 12 layers. It accepts paragraphs (i.e., word sequences of no more than 128 words) as input and outputs a probability for each category. We end up with two binary classifiers for each topic for a grand total of 16 classifiers: an assignment classifier that determines whether a paragraph expresses sentiment in a given topic domain and a sentiment classifier that then distinguishes positive and negative sentiments in a paragraph classified as belonging to a given topic domain. Thirdly, we run the 16 trained classifiers on each paragraph in our corpus and assign category probabilities to every paragraph. We then use the probabilities of all the paragraphs in an article to determine the article’s overall sentiment category (i.e., positive, negative, or neutral) in every topic.

As demonstrated in Table 1 , the two classifiers are accurate at both the paragraph and article levels. The assignment classifier and the sentiment classifier reach classification accuracy of 89–96% and 73–90%, respectively, on paragraphs. The combined outcome of the classifiers, namely article sentiment, is accurate to 62–91% across the eight topics. For comparison, a random guess would reach an accuracy of 50% on each task (see Supplementary Information for details).

American public opinion towards China is a composite measure drawn from national surveys that ask respondents for their opinions on China. We collect 101 cross-sectional surveys from 1974 to 2019 that asked relevant questions about attitudes toward China and incorporate a probabilistic model to harmonize different survey series with different scales (e.g., 4 levels, 10 levels) into a single time series, capitalizing on “seaming” years in which different survey series overlapped (Wang et al., 2021 ). For every year, there is a single real value representing American sentiment on China relative to the level in 1974. Put another way, we use sentiment in 1974 as a baseline measure to normalize the rest of the time series. A positive value shows a more favorable attitude than that in 1974, and a negative value represents a less favorable attitude than that in 1974. Because of this, the trends in sentiment changes year-over-year are of interest, but the absolute values of sentiment in a given year are not. As shown in Fig. 1 , public opinion towards China has varied greatly from 1974 to 2019. It steadily climbed from a low of −24% in 1976 to a high of 73% in 1987, and has fluctuated between 10% and 48% in the intervening 30 years.

This time series is aggregated from 101 cross-sectional surveys from 1974 to 2019 that asked relevant questions about attitudes toward China with the year of 1974 as baseline. Years with attitudes above zero show a more favorable attitude than that in 1974, with a peak of 73% in 1987. Years with attitudes below zero show a less favorable attitude than that in 1974, with the lowest level of −24% in 1976. The time series is shown with a 95% confidence interval.

We begin with a demonstration of how the reporting of The New York Times on China changes over time, and we follow this with an analysis of how coverage of China might influence public opinion toward China.

Trend of media sentiment

The New York Times has maintained a steady interest in China over the years and has published at least 3,000 articles on China in every year of our corpus. Figure 2 displays the yearly volume of China-related articles from The New York Times on each of the eight topics since 1970. Articles on China increased sharply after 2000 and eventually reached a peak around 2010, almost doubling their volume from the 1970s. As the number of articles on China increased, the amount of attention paid to each of the eight topics diverged. Articles on government, democracy, globalization, and culture were consistently common while articles on ideology were consistently rare. In contrast, articles on China’s economy, marketization, and welfare were rare before 1990 but became increasingly common after 2000. The timing of this uptick coincided neatly with worldwide recognition of China’s precipitous economic ascent and specifically the beginnings of China’s talks to join the World Trade Organization.

In each year we report in each topic the number of positive and negative articles while ignoring neutral/irrelevant articles. The media have consistently high attention on reporting China government & administration, democracy, globalization, and culture. There are emerging interests on China’s economics, marketization, and welfare and well-being since 1990s. Note that the sum of the stacks does not equal to the total volume of articles about China, because each article may express sentiment in none or multiple topics.

While the proportion of articles in each given topic change over time, the sentiment of articles in each topic is remarkably consistent. Ignoring neutral articles, Figure 3 illustrates the yearly fractions of positive and negative articles about each of the eight topics. We find four topics (economics, globalization, culture, and marketization) are almost always covered positively while reporting on the other four topics (ideology, government & administration, democracy, and welfare & well-being) is overwhelmingly negative.

The panel reports the trend of yearly media attitude toward China in ( A ) ideology, ( B ) government & administration, ( C ) democracy, ( D ) economic development, ( E ) marketization, ( F ) welfare & well-being, ( G ) globalization, and ( H ) culture. The media attitude is measured as the percentages of positive articles and negative articles, respectively. US–China relation milestones are marked as gray dots. The New York Times express diverging but consistent attitudes in the eight domains, with negative articles consistently common in ideology, government, democracy, and welfare, and positive sentiments common in economic, globalization, and culture. Standard errors are too small to be visible (below 1.55% in all topics all years).

The NYT views China’s globalization in a very positive light. Almost 100% of the articles mentioning this topic are positive for all of the years in our sample. This reveals that The New York Times welcomes China’s openness to the world and, more broadly, may be particularly partial to globalization in general.

Similarly, economics, marketization, and culture are covered most commonly in positive tones that have only grown more glowing over time. Positive articles on these topics began in the 1970s with China–US Ping–Pong diplomacy, and eventually comprise 1/4 to 1/2 of articles on these three topics, the remainder of which are mostly neutral articles. This agrees with the intuition that most Americans like Chinese culture. The New York Times has been deeply enamored with Chinese cultural products ranging from Chinese art to Chinese food since the very beginning of our sample. Following China’s economic reforms, the number of positive articles and the proportion of positive articles relative to negative articles increases for both economics and marketization.

In contrast, welfare and well-being are covered in an almost exclusively negative light. About 1/4 of the articles on this topic are negative, and almost no articles on this topic are positive. Topics regarding politics are covered very negatively. Negative articles on ideology, government and administration, and democracy outnumber positive articles on these topics for all of the years in our sample. Though small fluctuations that coincided with ebbs in US–China relations are observed for those three topics, coverage has only grown more negative over time. Government and administration is the only negatively covered topic that does feature some positive articles. This reflects the qualitative understanding that The New York Times thinks that the Chinese state is an unpleasant but capable actor.

Despite the remarkable diversity of sentiment toward China across the eight topics, sentiment within each of the topics is startlingly consistent over time. This consistency attests to the incredible stability of American stereotypes towards China. If there is any trend to be found here, it is that the main direction of sentiment in each topic, positive or negative, has grown more prevalent since the 1970s. This is to say that reporting on China has become more polarized, which is reflective of broader trends of media polarization (Jacobs and Shapiro, 2011 ; Mullainathan and Shleifer, 2005 ).

Media sentiment affects public opinion

To reveal the connection between media sentiment and public opinion, we run a linear regression model (Eq. ( 1 )) to fit public opinion with media sentiment from current and preceding years.

where μ t denotes public opinion in year t with possible values ranging from −1 to 1. F k j s is the fraction of positive ( s  = positive) or negative ( s  = negative) articles on topic k in year j . Coefficient β k j s quantifies the importance of F k j s in predicting μ t .

There is inertia to public opinion. A broadly held opinion is hard to change in the short term, and it may require a while for media sentiment to affect how the public views a given issue. For this reason, j is allowed to take [ t , t  − 1, t  − 2, ...] anywhere from zero to a couple of years ahead of t . In other words, we inspect lagged values of media sentiment as candidate predictors for public attitudes towards China.

We seek an optimal solution of media sentiment predictors to explain the largest fraction of variance ( r 2 ) of public opinion. To reduce the risk of overfitting, we first constrain the coefficients to be non-negative after reverse-coding negative sentiment variables, which means we assume that positive articles have either no impact or positive impact and that negative articles have either zero or negative impact on public opinion. Secondly, we require that the solution be sparse and contain no more than one non-zero coefficient in each topic:

where r 2 ( μ , β , F ) is the explained variance of μ fitted with ( β , F ). The l 0 -norm ∥ β k , ⋅ , ⋅ ∥ 0 gives the number of non-zero coefficients of topic k predictors.

The solution varies with the number of topics included in the fitting model. As shown in Table 2 , if we allow fitting with only one topic, we find that sentiment on Chinese culture has the most explanatory power, accounting for 31.2% of the variance in public opinion. We run a greedy strategy to add additional topics that yield the greatest increase in explanatory power, resulting in eight nested models (Table 2 ). The explanatory power of our models increases monotonically with the number of allowed topics but reaches a saturation point at which the marginal increase in variance explained per topics decreases after only two topics are introduced (see Table 2 ). To strike a balance between simplicity and explanatory power, we use the top two predictors, which are the positive sentiment of culture and the negative sentiment of democracy in the previous year, to build a linear predictor of public opinion that can be written as

where F culture, t −1,positive is the yearly fraction of positive articles on Chinese culture in year t  − 1 and F democracy, t −1,negative is the yearly fraction of negative articles on Chinese democracy in year t  − 1. This formula explains 53.9% of the variance of public opinion in the time series. For example, in 1993 53.9% of the articles on culture had a positive sentiment, and 46.9% of the articles on democracy had negative sentiment ( F c u l t u r e ,1993,positive  = 0.539, F democracy,1993,negative  = −0.469). Substituting those numbers into Eq. ( 2 ) predicts public opinion in the next year (1994) to be 0.208, very close to the actual level of public opinion (0.218) (Fig. 4 ).

The public opinion (solid), as a time series, is well fitted by the media sentiments on two selected topics, namely “Culture” and “Democracy”, in the previous year. The dashed line shows a linear prediction based on the fractions of positive articles on “Culture” and negative articles on “Democracy” in the previous year. The public opinion is shown with a 95% confidence interval, and the fitted line is shown with one standard error.

By analyzing a corpus of 267,907 articles from The New York Times with BERT, a state-of-the-art natural language processing model, we identify major shifts in media sentiment towards China across eight topic domains over 50 years and find that media sentiment leads public opinion. Our results show that the reporting of The New York Times on culture and democracy in one year explains 53.9% of the variation in public opinion on China in the next. The conclusion that we draw from our results is that media sentiment on China predicts public opinion on China. Our analysis is neither conclusive nor causal, but it is suggestive. Our results are best interpreted as a “reduced-form” description of the overall relationship between media sentiment and public opinion towards China.

While there are a number of potential factors that may complicate our conclusions, none would change the overall thrust of our results. We do not consider how the micro-level or meso-level intermediary processes through which opinion from elite media percolates to the masses below may affect our results. We also do not consider the potential ramifications of elites communing directly with the public, of major events in US–China relations causing short-term shifts in reporting, or of social media creating new channels for the diffusion of opinion. Finally, The New York Times might have a particular bias to how it covers China.

In addition to those specified above, a number of possible extensions of our work remain ripe targets for further research. Though a fully causal model of our text analysis pipeline may prove elusive (Egami et al., 2018 ), future work may use randomized vignettes to further our understanding of the causal effects of media exposure on attitudes towards China. Secondly, our modeling framework is deliberately simplified. The state affects news coverage before the news ever makes its way to the citizenry. It is plausible that multiple state-level actors may bypass the media and alter public opinion directly and to different ends. For example, the actions and opinions of individual high-profile US politicians may attenuate or exaggerate the impact of state-level tension on public sentiment toward China. There are presumably a whole host of intermediary processes through which opinion from elite media affects the sentiment of the masses. Thirdly, the relationship between the sentiment of The New York Times and public opinion may be very different for hot-button social issues of first-line importance in the American culture wars. In our corpus, The New York Times has covered globalization almost entirely positively, but the 2016 election of President Donald J. Trump suggests that many Americans do not share the zeal of The Times for international commerce. We also plan to extend our measure of media sentiment to include text from other newspapers. The Guardian, a similarly elite, Anglophonic, and left-leaning paper, will make for a useful comparison case. Finally, our analysis was launched in the midst of heightened tensions between the US and China and concluded right before the outbreak of a global pandemic. Many things have changed since COVID-19. Returning to our analysis with an additional year or two of data will almost certainly provide new results of additional interest.

Future work will address some of these additional paths, but none of these elements affects the basic conclusion of this work. We find that reporting on China in one year predicts public opinion in the next. This is true for more than fifty years in our sample, and while knowledge of, for example, the opinion diffusion process on social media may add detail to this relationship, the basic flow of opinion from media to the public will not change. Regarding the putative biases of The New York Times, its ideological slant does not affect our explanation of trends in public opinion of China as long as the paper’s relevant biases are relatively consistent over the time period covered by our analyses.

Data availability

All data analyzed during the current study are publicly available. The New York Times data were accessed using official online APIs ( https://developer.nytimes.com/ ). We used their query API to search for 267,907 articles that mention China, Chinese, Beijing, Peking, or Shanghai. We downloaded the full text and date of each article. The survey data were obtained from three large public archives/centers, namely Roper Center for Public Opinion Research (ROPER), NORC at the University of Chicago, and Pew Research Center (Pew Research Center, 2019 ; Smith et al., 2018 ). See Supplementary Information for a full list of surveys. The source codes and pretrained parameters of the natural language processing model BERT are publicly released by Google Inc. on its github repository ( https://github.com/google-research/bert ). The finetuned BERT models and the inferred sentiment of The New York Times articles in our corpus are publicly available at Princeton University DataSpace. Please check the project webpage ( http://www.attitudetowardchina.com/media-opinion ) or the DataSpace webpage ( https://doi.org/10.34770/x27d-0545 ) to download.

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Acknowledgements

The authors thank Chesley Chan (Princeton University), Wen Liu (Renmin University of China), Yichun Yang (Renmin University of China), and Emily Yin (Princeton University) for coding The New York Times articles with the topic-specific sentiment. The authors thank Chih-Jou Jay Chen (Academia Sinica), Cheng Cheng (Singapore Management University), Shawn Dorius (Iowa State University), Theodore P. Gerber (University of Wisconsin-Madison), Fengming Lu (Australian National University), Yongai Jin (Renmin University of China), Donghui Wang (Princeton University) for valuable discussions. The authors thank Xudong Guo (Tsinghua University) for helping fine-tune the BERT model and analyze the calculation results. The authors thank Tom Marling for proofreading the manuscript. The research was partially supported by the Paul and Marcia Wythes Center on Contemporary China at Princeton University and Guanghua School of Management at Peking University.

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Huang, J., Cook, G.G. & Xie, Y. Large-scale quantitative evidence of media impact on public opinion toward China. Humanit Soc Sci Commun 8 , 181 (2021). https://doi.org/10.1057/s41599-021-00846-2

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Best Social Media Research Topics | Inspiration & Ideas

Introduction

What distinguishes social media from other communication, why research social media, what can i research about social media, conducting research on social media.

Social media has become an integral part of modern communication, influencing how people connect, share information, and interact with the world. As a rapidly evolving field, it presents a wealth of opportunities for research that can offer valuable insights into societal trends, behavioral patterns, and technological advancements. This article aims to provide inspiration and ideas for selecting compelling social media research topics. We’ll explore what makes social media unique, the importance of studying it, and offer suggestions for areas you can investigate.

Social media is a unique form of communication that differs significantly from traditional methods such as face-to-face interactions, phone calls, or even emails. Several key characteristics set social media apart, making it a fascinating area for research.

Interactivity and user-generated content

One of the most distinctive features of social media is its interactivity. Unlike traditional media, where communication is typically one-way, social media platforms enable two-way interactions between users. This interactivity allows users to not only consume content but also to create and share their own, leading to an environment rich in user-generated content. This aspect of social media fosters a participatory culture where individuals can contribute to discussions, share their perspectives, and engage with others in real-time.

Networked communication

Social media operates on a networked model of communication, where information is shared across a web of interconnected users. This networked nature allows content to spread rapidly through shares, likes, comments, and other forms of engagement. The viral potential of social media content is a key characteristic that distinguishes it from other forms of communication, where information dissemination is often more controlled and linear.

Personalization and algorithms

Another defining feature of social media is the use of algorithms to personalize user experiences. These algorithms analyze user behavior, preferences, and interactions to curate content that is most relevant to each individual. This level of personalization is unmatched by traditional communication methods and has profound implications for how people receive information, form opinions, and engage with the world around them. The algorithm-driven nature of social media also raises important questions about echo chambers, filter bubbles, and the impact of personalized content on societal discourse.

Multimedia integration

Social media platforms seamlessly integrate various forms of multimedia, including text, images, videos , and live streams. This multimedia approach enhances the richness of social media communication and allows users to express themselves in diverse and creative ways. The ability to combine different media types in a single platform sets social media apart from other communication methods, which may be more limited in their use of media.

Global reach and immediacy

Finally, a solid social media presence offers unprecedented global reach and immediacy. With social media exposure, users can connect with others across the world instantly, breaking down geographical barriers and enabling cross-cultural communication. The real-time nature of social media allows for immediate responses and updates, making it a powerful tool for sharing news, organizing events, and mobilizing movements for marketing endeavors, political campaigns, and other collective efforts.

Researching social media is crucial because of its pervasive influence on various aspects of society, including communication, culture, politics, and even mental health. As social media continues to evolve and integrate into everyday life, understanding its impact becomes increasingly important for several reasons.

First, social media shapes public opinion and discourse in ways that traditional media cannot. The speed at which information spreads on platforms like Twitter/X, Facebook, and Instagram can amplify voices and ideas, often creating significant cultural or political movements. Studying these phenomena can reveal insights into how public opinion is formed, how misinformation spreads, and how social movements gain traction.

Second, social media platforms collect vast amounts of data about user behavior, preferences, and interactions. This data offers a unique opportunity for researchers to analyze trends, understand user engagement, and explore the effects of algorithmic content curation. By examining these aspects, researchers can shed light on how social media influences decision-making, consumer behavior, and even voting patterns.

Moreover, social media has a profound impact on mental health and well-being. The constant connectivity and exposure to curated lives can lead to issues such as anxiety, depression, and feelings of inadequacy. Research in this area can help identify the factors contributing to these mental health challenges and guide the development of interventions or policies to mitigate them.

Finally, as social media becomes a key tool for marketing, education, and even governance, understanding its mechanisms and effects is vital for professionals across various fields. Whether it’s to improve social media marketing strategies, enhance educational outreach, or design more effective public policies, social media research papers provide valuable insights that can inform practice and policy.

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Choosing a social media research topic can be a difficult decision among numerous research opportunities across various disciplines. Here are three key areas to consider when selecting a research topic related to social media: societal impact, psychological effects, and technological advancements.

Societal impact of social media

One of the most significant aspects of social media is its profound impact on society. Researching this area can provide valuable insights into how social media influences cultural norms, political movements, and social behavior.

• Social media and political movements : Social media platforms have played a crucial role in organizing and mobilizing political movements around the world. From the Arab Spring to the Black Lives Matter movement, these platforms have facilitated the rapid spread of information and coordination among activists. Researching the role of social media in political movements can reveal how these platforms influence public opinion, empower grassroots movements, and even shape election outcomes. Additionally, you can explore the potential downsides, such as the spread of misinformation or the role of bots and fake accounts in manipulating political discourse.
• Cultural diversity and social media : Social media platforms are global in reach, connecting people from different cultures and backgrounds. This connectivity can promote cultural diversity by exposing users to new ideas, traditions, and perspectives. However, it can also lead to cultural homogenization, where dominant cultures overshadow minority voices. Researching the impact of social media on cultural diversity can explore how these platforms either promote or hinder cultural exchange and the preservation of cultural identities. You might also investigate the role of social media in fostering cross-cultural understanding or exacerbating cultural tensions.
• Social media and public health campaigns : Social media has become a vital tool for public health communication, particularly during global crises like the COVID-19 pandemic. Platforms like Twitter/X and Facebook have been used to disseminate important health information, raise awareness about preventive measures, and combat misinformation. Researching the effectiveness of social media in public health campaigns can provide insights into how these platforms can be used to promote healthy behaviors, increase vaccination rates, and improve public health outcomes. Additionally, you can examine the challenges of combating health misinformation and the social media strategies that have been successful in addressing it.

Psychological effects of social media

In studying social media, psychology has many potential theoretical and practical research questions . Understanding how these platforms influence mental health, self-esteem, and social interactions is crucial for developing strategies to mitigate negative impacts and enhance positive outcomes.

• Social media and mental health : One of the most extensively studied areas is the relationship between social media use and mental health. Research has shown that excessive use of social media can lead to negative outcomes such as anxiety, depression, and loneliness. However, these effects can vary depending on factors like age, personality, and the type of content consumed. Researching the impact of social media on mental health can involve exploring the specific mechanisms through which social media affects well-being, such as comparison with others, cyberbullying, or the pressure to present a perfect image online. You might also investigate the potential benefits of social media, such as providing support networks for individuals with mental health challenges.
• The role of social media in shaping self-identity : Social media platforms provide users with the tools to curate and present their identities online. This process of identity construction can have both positive and negative effects. On the one hand, social media can empower individuals to express themselves and connect with like-minded communities. On the other hand, the pressure to conform to societal standards and the constant exposure to idealized images can lead to issues like low self-esteem and body image concerns. Researching the role of social media in shaping self-identity can involve examining how different groups (e.g., teenagers, adults, marginalized communities) use social media to explore and express their identities. Additionally, you can study the impact of social media on self-perception and the ways in which online interactions influence offline behaviors and attitudes.
• Social media addiction : As social media becomes increasingly integrated into daily life, the phenomenon of social media addiction has garnered significant attention. Social media addiction is characterized by excessive use of social media platforms, leading to negative consequences in an individual's personal, academic, or professional life. Researching social media addiction can involve exploring the factors that contribute to addictive behaviors, such as the design of social media platforms, individual personality traits, and social influences. Additionally, you can investigate the impact of social media addiction on mental health, relationships, and productivity, as well as potential interventions to address this issue.

Technological advancements and social media

Technological advancements play a pivotal role in shaping the evolution of social media platforms. Understanding these advancements and their implications can offer valuable insights into the future of social media and its impact on society.

• Artificial intelligence and social media algorithms : Artificial intelligence (AI) is increasingly being used to power the algorithms that drive content curation on social media platforms. These algorithms analyze user behavior, preferences, and interactions to deliver personalized content, ads, and recommendations. While AI can enhance user experience by providing relevant content, it also raises concerns about privacy, echo chambers, and the manipulation of information. Researching the role of AI in social media can involve exploring how these algorithms work, their impact on user behavior, and the ethical implications of AI-driven content curation. Additionally, you can study the potential of AI to combat issues like misinformation, hate speech, and online harassment.
• The evolution of social media platforms : Social media platforms are constantly evolving, with new features, tools, and platforms emerging regularly. Understanding the technological trends driving these changes can provide insights into the future of social media. Researching the evolution of social media platforms can involve examining how new technologies, such as augmented reality (AR), virtual reality (VR), and live streaming, are being integrated into social media. You can also explore the impact of these technologies on user engagement, content creation, and social interactions. Additionally, consider investigating the rise of niche social media platforms and how they cater to specific communities or interests.
• Data privacy and security on social media : As social media platforms collect vast amounts of user data, concerns about data privacy and security have become increasingly prominent. Researching data privacy on social media can involve exploring the ways in which platforms collect, store, and use user data, as well as the potential risks associated with data breaches and unauthorized access. Additionally, you can examine the impact of data privacy regulations, such as the General Data Protection Regulation (GDPR), on social media platforms and their practices. Studying user perceptions of data privacy and their behaviors in response to privacy concerns can also provide valuable insights into how social media platforms can build trust with their users.

Conducting research on social media requires careful consideration of the methodologies employed, the ethical implications involved, and the approaches to data analysis. Each of these factors plays a crucial role in ensuring that the research is both rigorous and responsible.

Choosing appropriate methodologies

Selecting the appropriate research methodology is a foundational step in addressing social media research questions . The choice of methodology largely depends on the research questions and objectives. Qualitative methods, such as in-depth interviews , focus groups , and content analysis , offer valuable insights into the subjective experiences and perceptions of social media users.

For example, interviews can reveal how individuals construct and present their identities online, while content analysis allows researchers to explore patterns and themes within social media interactions, such as how users discuss specific topics or respond to particular events.

On the other hand, quantitative methods, like surveys and experiments, are essential for gathering data that can be measured and analyzed statistically. Surveys can provide a broad overview of user behaviors and attitudes across large populations, enabling researchers to identify trends and correlations. Experiments, meanwhile, are useful for testing specific hypotheses, such as the impact of social media use on academic performance or mental health.

In some cases, combining qualitative and quantitative methods in a mixed-methods approach can offer a more comprehensive understanding of the phenomena being studied, allowing researchers to explore both the depth and breadth of social media interactions.

Accounting for ethical issues

Ethical considerations are paramount in social media research, given the sensitive nature of the data often involved. One of the primary ethical challenges is obtaining informed consent from participants , especially in environments where users may not be fully aware that their posts or interactions are being analyzed.

Researchers must navigate this challenge by ensuring that their methods of obtaining consent are clear and transparent, particularly when dealing with content that users might consider private, despite being posted on public platforms.

Protecting the privacy and confidentiality of participants is another critical concern. This involves anonymizing data to prevent the identification of individuals and securing the data to protect it from unauthorized access. Researchers must also be sensitive to the potential risks associated with their studies, particularly when dealing with vulnerable populations or sensitive topics such as mental health or political beliefs.

Transparency in the research process is essential; researchers should openly communicate their intentions, methods, and any potential conflicts of interest, ensuring that participants understand how their data will be used. Engaging with the communities involved in the research can also help to mitigate ethical concerns, as it fosters trust and collaboration, making the research process more inclusive and respectful of participants' rights and perspectives.

Conducting data analysis

The analysis of social media data presents its own set of challenges, given the vast amount of information that can be generated on these platforms. Effective data analysis requires not only technical proficiency but also a deep understanding of the social context in which the data is produced.

Data cleaning and preparation are crucial initial steps, as social media data often contains noise and irrelevant information that can skew results. Researchers must carefully filter and organize their data to ensure that the analysis is accurate and meaningful. Once the data is prepared, researchers can apply various analytical techniques, depending on the research objectives.

For qualitative data , thematic analysis can be used to identify recurring themes and patterns within the content, providing insights into user behaviors and perceptions. Quantitative data , on the other hand, may require statistical analysis to uncover correlations, trends, or causal relationships.

Throughout the analysis process, it is important for researchers to remain mindful of the limitations of their data and the potential biases that may influence their findings. This includes being aware of the algorithms that social media platforms use to curate content, which can impact the data collected and the conclusions drawn from it.

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Masturbation paper researcher attacks ‘groupthink’ backlash

Two years on from international scandal, karl andersson claims he had received nothing but praise for article prior to online outcry.

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A PhD student who was booted off his course after writing a paper describing how he masturbated to sexualised images of young boys has accused his university and publisher of surrendering to an online pile-on.

Karl Andersson was expelled by the University of Manchester and his 2022 article – subtitled “Using masturbation as an ethnographic method in research on shota subculture in Japan” – was retracted after his exploration of a manga comic genre depicting sexual encounters involving children was condemned as “morally offensive”.

The Swedish researcher has never spoken publicly about the saga, but in a new book, Impossibly Cute Boys: The Healing Power of Shota Comics in Japan , he claims that he had received nothing but praise for his paper prior to publication.

The convener of a PhD course in “queer autoethnography” where the paper was drafted praised it as a “wonderfully written, reflective, analytical and intriguing essay on masturbation ‘in the field’”, adding: “This is already very publishable, should you so desire. Bravo,” Mr Andersson recounts.

He says that his own supervisor praised it as “pretty damn good”, describing it as his “best piece of writing”, while it was positively received on submission to the Sage journal Qualitative Research , with one reviewer stating that the rationale for using masturbation as a method was “well justified”, adding: “The author has conducted provocative research by use of a highly bold and innovative application of autoethnography. Best of all, the author has done this extremely well.”

However, after the published paper made headlines around the world, it was pulled by Sage, which said it “legitimises sexual activity involving sexually graphic illustrated images of children” and had “potential to cause significant harm”, while Manchester – which had funded Mr Andersson’s PhD – expelled him after ruling that he had caused “significant reputational harm” to the university.

Mr Andersson, who writes that he is now an independent researcher, claims that he fell victim to “ill-willed and homophobic reports” by the media and accusations from “the mob” on social media, with “otherwise rational academics” joining in as an act of “groupthink”.

But Michelle Shipworth, an associate professor in UCL’s Energy Institute who was an early critic of the paper, said it was concerning – and telling – that nobody had challenged Mr Andersson’s research topic or methods.

“Academia is becoming more attitudinally homogenous and, at the same time, more censorious, making it increasingly dangerous for academics to argue against a view they believe is widely held,” she said. “This creates a risky environment vulnerable to both accidents and exploitation.”

Manchester said that it had conducted a “robust” investigation into the “significant concerns” around Mr Andersson’s work.

A Sage spokesperson said: “Together with the editors of the journal, we retracted the article after an investigation determined that there was a lack of institutional ethical oversight and a lack of adequate and appropriate ethical review ahead of publication.”

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