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Social media harms teens’ mental health, mounting evidence shows. what now.

Understanding what is going on in teens’ minds is necessary for targeted policy suggestions

A teen scrolls through social media alone on her phone.

Most teens use social media, often for hours on end. Some social scientists are confident that such use is harming their mental health. Now they want to pinpoint what explains the link.

Carol Yepes/Getty Images

Increasing rigor

Concerns over the effects of social media use in children have been circulating for years, resulting in a massive body of scientific literature. But those mostly correlational studies could not show if teen social media use was harming mental health or if teens with mental health problems were using more social media.

Moreover, the findings from such studies were often inconclusive, or the effects on mental health so small as to be inconsequential. In one study that received considerable media attention, psychologists Amy Orben and Andrew Przybylski combined data from three surveys to see if they could find a link between technology use, including social media, and reduced well-being. The duo gauged the well-being of over 355,000 teenagers by focusing on questions around depression, suicidal thinking and self-esteem.

Digital technology use was associated with a slight decrease in adolescent well-being , Orben, now of the University of Cambridge, and Przybylski, of the University of Oxford, reported in 2019 in Nature Human Behaviour . But the duo downplayed that finding, noting that researchers have observed similar drops in adolescent well-being associated with drinking milk, going to the movies or eating potatoes.

Holes have begun to appear in that narrative thanks to newer, more rigorous studies.

In one longitudinal study, researchers — including Orben and Przybylski — used survey data on social media use and well-being from over 17,400 teens and young adults to look at how individuals’ responses to a question gauging life satisfaction changed between 2011 and 2018. And they dug into how the responses varied by gender, age and time spent on social media.

Social media use was associated with a drop in well-being among teens during certain developmental periods, chiefly puberty and young adulthood, the team reported in 2022 in Nature Communications . That translated to lower well-being scores around ages 11 to 13 for girls and ages 14 to 15 for boys. Both groups also reported a drop in well-being around age 19. Moreover, among the older teens, the team found evidence for the Goldilocks Hypothesis: the idea that both too much and too little time spent on social media can harm mental health.

“There’s hardly any effect if you look over everybody. But if you look at specific age groups, at particularly what [Orben] calls ‘windows of sensitivity’ … you see these clear effects,” says L.J. Shrum, a consumer psychologist at HEC Paris who was not involved with this research. His review of studies related to teen social media use and mental health is forthcoming in the Journal of the Association for Consumer Research.

Cause and effect

That longitudinal study hints at causation, researchers say. But one of the clearest ways to pin down cause and effect is through natural or quasi-experiments. For these in-the-wild experiments, researchers must identify situations where the rollout of a societal “treatment” is staggered across space and time. They can then compare outcomes among members of the group who received the treatment to those still in the queue — the control group.

That was the approach Makarin and his team used in their study of Facebook. The researchers homed in on the staggered rollout of Facebook across 775 college campuses from 2004 to 2006. They combined that rollout data with student responses to the National College Health Assessment, a widely used survey of college students’ mental and physical health.

The team then sought to understand if those survey questions captured diagnosable mental health problems. Specifically, they had roughly 500 undergraduate students respond to questions both in the National College Health Assessment and in validated screening tools for depression and anxiety. They found that mental health scores on the assessment predicted scores on the screenings. That suggested that a drop in well-being on the college survey was a good proxy for a corresponding increase in diagnosable mental health disorders.

Compared with campuses that had not yet gained access to Facebook, college campuses with Facebook experienced a 2 percentage point increase in the number of students who met the diagnostic criteria for anxiety or depression, the team found.

When it comes to showing a causal link between social media use in teens and worse mental health, “that study really is the crown jewel right now,” says Cunningham, who was not involved in that research.

A need for nuance

The social media landscape today is vastly different than the landscape of 20 years ago. Facebook is now optimized for maximum addiction, Shrum says, and other newer platforms, such as Snapchat, Instagram and TikTok, have since copied and built on those features. Paired with the ubiquity of social media in general, the negative effects on mental health may well be larger now.

Moreover, social media research tends to focus on young adults — an easier cohort to study than minors. That needs to change, Cunningham says. “Most of us are worried about our high school kids and younger.”

And so, researchers must pivot accordingly. Crucially, simple comparisons of social media users and nonusers no longer make sense. As Orben and Przybylski’s 2022 work suggested, a teen not on social media might well feel worse than one who briefly logs on.

Researchers must also dig into why, and under what circumstances, social media use can harm mental health, Cunningham says. Explanations for this link abound. For instance, social media is thought to crowd out other activities or increase people’s likelihood of comparing themselves unfavorably with others. But big data studies, with their reliance on existing surveys and statistical analyses, cannot address those deeper questions. “These kinds of papers, there’s nothing you can really ask … to find these plausible mechanisms,” Cunningham says.

One ongoing effort to understand social media use from this more nuanced vantage point is the SMART Schools project out of the University of Birmingham in England. Pedagogical expert Victoria Goodyear and her team are comparing mental and physical health outcomes among children who attend schools that have restricted cell phone use to those attending schools without such a policy. The researchers described the protocol of that study of 30 schools and over 1,000 students in the July BMJ Open.

Goodyear and colleagues are also combining that natural experiment with qualitative research. They met with 36 five-person focus groups each consisting of all students, all parents or all educators at six of those schools. The team hopes to learn how students use their phones during the day, how usage practices make students feel, and what the various parties think of restrictions on cell phone use during the school day.

Talking to teens and those in their orbit is the best way to get at the mechanisms by which social media influences well-being — for better or worse, Goodyear says. Moving beyond big data to this more personal approach, however, takes considerable time and effort. “Social media has increased in pace and momentum very, very quickly,” she says. “And research takes a long time to catch up with that process.”

Until that catch-up occurs, though, researchers cannot dole out much advice. “What guidance could we provide to young people, parents and schools to help maintain the positives of social media use?” Goodyear asks. “There’s not concrete evidence yet.”

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

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1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
4 CETYS Universidad, Tijuana, Mexico
5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

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Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

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Pros & cons: impacts of social media on mental health

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

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

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

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

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

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

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

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

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Acknowledgements

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

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AZ conceived and drafted the Editorial. MESR wrote the abstract and revised the Editorial. All authors read and approved the final manuscript.

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Social Media Use and Impact on Interpersonal Communication

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Yerika Jimenez 2 &
Patricia Morreale 3

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This research paper presents the findings of a research project that investigated how young adult interpersonal communications have changed since using social media. Specifically, the research focused on determining if using social media had a beneficial or an adverse effect on the development of interaction and communication skills of young adults. Results from interviews reveal a negative impact in young adult communications and social skills. In this paper young adult preferences in social media are also explored, to answer the question: Does social media usage affect the development of interaction and communication skills for young adults and set a basis for future adult communication behaviors?

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Weibo or Weixin? Gratifications for Using Different Social Media

Social Media Dependency: The Implications of Technological Communication Use Among University Students

Investigating the Usage Patterns and the Implications of Young Adults’ Social Media Usage in South Africa

Social media
Social interaction
Interpersonal communications
Young adults

1 Introduction

Human interaction has changed drastically in the last 20 years, not only due to the introduction of the Internet, but also from social media and online communities. These social media options and communities have grown from being simply used to communicate on a private network into a strong culture that almost all individuals are using to communicate with others all over the world. We will concentrate on the impact that social media has on human communication and interaction among young adults, primarily college students. In today’s society, powerful social media platforms such as Myspace, Facebook, Twitter, Instagram (IG), and Pinterest have been the result of an evolution that is changing how humans communicate with each other. The big question we asked ourselves was how much has social media really impacted the way that humans communicate and interact with each other, and if so, how significant is the change of interpersonal interaction among young adults in the United States today?

The motivation behind this research has been personal experience with interaction and communication with friends and family; it had become difficult, sometimes even rare, to have a one-on-one conversation with them, without having them glancing at or interacting with their phone. Has social interaction changed since the introduction of advanced technology and primarily social media? In correlation with the research data collected in this study, it was concluded that many participants’ personal communication has decreased due social media influence encouraging them to have online conversations, as opposed to face-to-face, in-person conversations.

2 Related Work

The question of how social media affects social and human interaction in our society is being actively researched and studied. A literature review highlights the positive and negative aspects of social media interaction, as researchers battle to understand the current and future effects of social media interaction. A study done by Keith Oatley, an emeritus professor of cognitive psychology at the University of Toronto, suggests that the brain may interpret digital interaction in the same manner as in-person interaction, while others maintain that differences are growing between how we perceive one another online as opposed to in reality [ 1 ]. This means that young adults can interpret online communication as being real one-on-one communication because the brain will process that information as a reality. Another study revealed that online interaction helps with the ability to relate to others, tolerate differing viewpoints, and express thoughts and feeling in a healthy way [ 2 , 3 ]. Moreover a study executed by the National Institutes of Health found that youths with strong, positive face-to-face relationships may be those most frequently using social media as an additional venue to interact with their peers [ 4 ].

In contrast, research reveals that individuals with many friends may appear to be focusing too much on Facebook, making friends out of desperation rather than popularity, spending a great deal of time on their computer ostensibly trying to make connections in a computer-mediated environment where they feel more comfortable rather than in face-to-face social interaction [ 5 ]. Moreover, a study among college freshman revealed that social media prevents people from being social and networking in person [ 6 ].

3 Experimental Design

This research study was divided into two parts during the academic year 2013–2014. Part one, conducted during fall semester 2013, had the purpose of understanding how and why young adults use their mobile devices, as well as how the students describe and identify with their mobile devices. This was done by distributing an online survey to several Kean University student communities: various majors, fraternity and sorority groups, sports groups, etc. The data revealed that users primarily used their mobile devices for social media and entertainment purposes. The surveyed individuals indicated that they mainly accessed mobile apps like Facebook, Pinterest, Twitter, and Instagram, to communicate, interact, and share many parts of their daily life with their friends and peers.

Based on the data collected during part one, a different approach and purpose was used for part two, with the goal being to understand how social media activities shape the communication skills of individuals and reflects their attitudes, attention, interests, and activities. Additionally, research included how young adult communication needs change through the use of different social media platforms, and if a pattern can be predicted from the users’ behavior on the social media platforms. Part two of this research was conducted by having 30 one-on-one interviews with young adults who are college students. During this interview key questions were asked in order to understand if there is a significant amount of interpersonal interaction between users and their peers. Interpersonal interaction is a communication process that involves the exchange of information, feelings and meaning by means of verbal or non-verbal messages. For the purposes of this paper, only the data collected during spring 2014 is presented.

4 Data Collection

Through interviews, accurate results of the interaction of young adults with social media were collected. These interviews involved 30 one-on-one conversations with Kean University students. Having one-on-one interviews with participants allowed for individual results, first responses from the participant, without permitting responses being skewed or influenced by other participants, such as might occur in group interviews. It also allows users to give truthful answers, in contrast to an online or paper survey, as they might have second thoughts about an answer and change it. The one-on-one interviews consisted of ten open-ended questions, which were aimed to answer, and ultimately determine, how social media interaction involuntarily influences, positively or negatively, an individual’s attitude, attention, interests, and social/personal activities. The largest motive behind the questions was to determine how individual communication skills, formally and informally, have changed from interacting with various social media platforms. The interviews, along with being recorded on paper, were also video and audio-recorded. The average time for each interview was between two to ten minutes. These interviews were held in quiet labs and during off-times, so that the responses could be given and recorded clearly and without distraction (Fig. 1 ). A total of 19 females and 11 males participated, with ages ranging from 19 to 28 years old.

Female participant during one-on-one interview

After conducting the interviews and analyzing the data collected, it was determined that the age when participants, both male and female, first began to use social media ranged between 9 to 17 years. It was found that, generally, males began to use social media around the age of 13, whereas females started around the age of 12. The average age for males starting to use social media is about 12.909 with a standard deviation of 2.343. For females, the average age is 12.263 with a standard deviation of 1.627. From this, we can determine that males generally begin to use social media around the age of 13, whereas females begin around the age of 12.

After determining the average age of when participants started using social media, it was necessary to find which social media platforms they had as a basis; meaning which social media platform they first used. MySpace was the first social media used by twenty-three participants, followed by Facebook with three users, and Mi Gente by only one user, with two participants not using social media at all. It was interesting to find that all of the participants who started using Myspace migrated to Facebook. The reasoning provided was that “everyone [they knew] started to use Facebook.” According to the participants, Facebook was “more interactive” and was “extremely easy to use.” The participants also stated that Myspace was becoming suitable for a younger user base, and it got boring because they needed to keep changing their profile backgrounds and modifying their top friends, which caused rifts or “popularity issues” between friends. After finding out which platform they started from, it was also essential to find out which platform they currently use. However, one platform that seemed to be used by all participants to keep up-to-date with their friends and acquaintances was Instagram, a picture and video-based social media platform. Another surprising finding was that many users did not use Pinterest at all, or had not even heard of the platform. After determining which social media platforms the users migrated to, it was essential to identify what caused the users to move from one platform to another. What are the merits of a certain platform that caused the users to migrate to it, and what are the drawbacks of another platform that caused users to migrate from it or simply not use it all?

4.1 Social Interaction Changes

For some participants social interaction had a chance for a positive outcome, while others viewed it in a more negative aspect. The participants were asked if their social interactions have changed since they were first exposed to social media (Table 1 ). One participant stated that “it is easier to just look at a social media page to see how friends and family are doing rather than have a one-on-one interaction.” As for people’s attitudes, they would rather comment or “like” a picture than stop and have a quick conversation. On the other hand, another participant felt that social media helped them when talking and expressing opinions on topics that they generally would not have discussed in person. Moreover, the participants are aware of the actions and thing that they are doing but continue to do it because they feel comfortable and did not desire to have one-on-one interactions with people.

The participants were also asked to explain how social media changed their communication and interactions during the years of using social media (Table 2 ). The data shows that participants interact less in person because they are relating more via online pictures and status. For other participants, it made them more cautious and even afraid of putting any personal information online because it might cause problems or rifts in their life. On the contrary, some participants stated that their communication and interaction is the same; however, they were able to see how it had changed for the people that are around them. A participant stated that “internet/social media is a power tool that allows people to be whatever they want and in a way it creates popularity, but once again they walk around acting like they do not know you and ‘like’ your pictures the next day.”

5 Discussion

The data illustrated in this paper shows how much the introduction and usage of social media has impacted the interaction and communication of young adults. The future of interaction and communication was also presented as a possibility, if the current trend continues with young adults and social media or online communities. This raises the notion of possibly not having any social, in-person interaction and having all communication or interaction online and virtually with all family and friends.

6 Conclusion

Referring back to the question asked during the introduction: how much has social media impacted the way we communicate and interact with each other? After reviewing all the findings, seeing the relationship individuals have with their mobile phones, and comparing social media platforms, it is clear that many young adults have an emotional attachment with their mobile device and want interaction that is quick and to the point, with minimal “in-person” contact. Many young adults prefer to use their mobile device to send a text message or interact via social media. This is due to their comfort level being higher while posting via social media applications, as opposed to in-person interaction. To successfully and accurately answer the question: yes, social media has had a very positive and negative effect on the way we communicate and interact with each other. However, how effective is this method of “virtual” communication and interaction in the real world?

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Yerika Jimenez

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Constantine Stephanidis

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Jimenez, Y., Morreale, P. (2015). Social Media Use and Impact on Interpersonal Communication. In: Stephanidis, C. (eds) HCI International 2015 - Posters’ Extended Abstracts. HCI 2015. Communications in Computer and Information Science, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-21383-5_15

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Thesis Statements about Social Media: 21 Examples and Tips

by Judy Jeni
January 27, 2024

Writing Thesis Statements Based On Social Media

A thesis statement is a sentence in the introduction paragraph of an essay that captures the purpose of the essay. Using thesis statements about social media as an example, I will guide you on how to write them well.

It can appear anywhere in the first paragraph of the essay but it is mostly preferred when it ends the introduction paragraph. learning how to write a thesis statement for your essay will keep you focused.

A thesis statement can be more than one sentence only when the essay is on complex topics and there is a need to break the statement into two. This means, a good thesis statement structures an essay and tells the reader what an essay is all about.

A good social media thesis statement should be about a specific aspect of social media and not just a broad view of the topic.

The statement should be on the last sentence of the first paragraph and should tell the reader about your stand on the social media issue you are presenting or arguing in the essay.

Reading an essay without a thesis statement is like solving a puzzle. Readers will have to read the conclusion to at least grasp what the essay is all about. It is therefore advisable to craft a thesis immediately after researching an essay.

Throughout your entire writing, every point in every paragraph should connect to the thesis. In case it doesn’t then probably you have diverged from the main issue of the essay.

How to Write a Thesis Statement?

Writing a thesis statement is important when writing an essay on any topic, not just about social media. It is the key to holding your ideas and arguments together into just one sentence.

The following are tips on how to write a good thesis statement:

Start With a Question and Develop an Answer

If the question is not provided, come up with your own. Start by deciding the topic and what you would like to find out about it.

Secondly, after doing some initial research on the topic find the answers to the topic that will help and guide the process of researching and writing.

Consequently, if you write a thesis statement that does not provide information about your research topic, you need to construct it again.

Be Specific

The main idea of your essay should be specific. Therefore, the thesis statement of your essay should not be vague. When your thesis statement is too general, the essay will try to incorporate a lot of ideas that can contribute to the loss of focus on the main ideas.

Similarly, specific and narrow thesis statements help concentrate your focus on evidence that supports your essay. In like manner, a specific thesis statement tells the reader directly what to expect in the essay.

Make the Argument Clear

Usually, essays with less than one thousand words require the statement to be clearer. Remember, the length of a thesis statement should be a single sentence, which calls for clarity.

In these short essays, you do not have the freedom to write long paragraphs that provide more information on the topic of the essay.

Likewise, multiple arguments are not accommodated. This is why the thesis statement needs to be clear to inform the reader of what your essay is all about.

If you proofread your essay and notice that the thesis statement is contrary to the points you have focused on, then revise it and make sure that it incorporates the main idea of the essay. Alternatively, when the thesis statement is okay, you will have to rewrite the body of your essay.

Question your Assumptions

Before formulating a thesis statement, ask yourself the basis of the arguments presented in the thesis statement.

Assumptions are what your reader assumes to be true before accepting an argument. Before you start, it is important to be aware of the target audience of your essay.

Thinking about the ways your argument may not hold up to the people who do not subscribe to your viewpoint is crucial.

Alongside, revise the arguments that may not hold up with the people who do not subscribe to your viewpoint.

Take a Strong Stand

A thesis statement should put forward a unique perspective on what your essay is about. Avoid using observations as thesis statements.

In addition, true common facts should be avoided. Make sure that the stance you take can be supported with credible facts and valid reasons.

Equally, don’t provide a summary, make a valid argument. If the first response of the reader is “how” and “why” the thesis statement is too open-ended and not strong enough.

Make Your Thesis Statement Seen

The thesis statement should be what the reader reads at the end of the first paragraph before proceeding to the body of the essay. understanding how to write a thesis statement, leaves your objective summarized.

Positioning may sometimes vary depending on the length of the introduction that the essay requires. However, do not overthink the thesis statement. In addition, do not write it with a lot of clever twists.

Do not exaggerate the stage setting of your argument. Clever and exaggerated thesis statements are weak. Consequently, they are not clear and concise.

Good thesis statements should concentrate on one main idea. Mixing up ideas in a thesis statement makes it vague. Read on how to write an essay thesis as part of the steps to write good essays.

A reader may easily get confused about what the essay is all about if it focuses on a lot of ideas. When your ideas are related, the relation should come out more clearly.

21 Examples of Thesis Statements about Social Media

Recently, social media is growing rapidly. Ironically, its use in remote areas has remained relatively low.
Social media has revolutionized communication but it is evenly killing it by limiting face-to-face communication.
Identically, social media has helped make work easier. However,at the same time it is promoting laziness and irresponsibility in society today.
The widespread use of social media and its influence has increased desperation, anxiety, and pressure among young youths.
Social media has made learning easier but its addiction can lead to bad grades among university students.
As a matter of fact, social media is contributing to the downfall of mainstream media. Many advertisements and news are accessed on social media platforms today.
Social media is a major promoter of immorality in society today with many platforms allowing sharing of inappropriate content.
Significantly, social media promotes copycat syndrome that positively and negatively impacts the behavior adapted by different users.
In this affluent era, social media has made life easy but consequently affects productivity and physical strength.
The growth of social media and its ability to reach more people increases growth in today’s business world.
The freedom on social media platforms is working against society with the recent increase in hate speech and racism.
Lack of proper verification when signing up on social media platforms has increased the number of minors using social media exposing them to cyberbullying and inappropriate content.
The freedom of posting anything on social media has landed many in trouble making the need to be cautious before posting anything important.
The widespread use of social media has contributed to the rise of insecurity in urban centers
Magazines and journals have spearheaded the appreciation of all body types but social media has increased the rate of body shaming in America.
To stop abuse on Facebook and Twitter the owners of these social media platforms must track any abusive post and upload and ban the users from accessing the apps.
Social media benefits marketing by creating brand recognition, increasing sales, and measuring success with analytics by tracking data.
Social media connects people around the globe and fosters new relationships and the sharing of ideas that did not exist before its inception.
The increased use of social media has led to the creation of business opportunities for people through social networking, particularly as social media influencers.
Learning is convenient through social media as students can connect with education systems and learning groups that make learning convenient.
With most people spending most of their free time glued to social media, quality time with family reduces leading to distance relationships and reduced love and closeness.

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Social Media: Thesis Statement

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Positive effects, negative effects, positive social change.

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

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

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

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

But where do you begin? It can be challenging to know where to start with so much information available. Jenni.ai comes in handy here. Jenni.ai is an AI application built exclusively for students to help them write essays more quickly and easily. Jenni.ai provides students with inspiration and assistance on how to approach their essays with its enormous database of sample essays on a variety of themes, including social media. Jenni.ai is the solution you've been looking for if you're experiencing writer's block or need assistance getting started.

So, whether you're a student looking to better your essay writing skills or want to remain up to date on the latest social media advancements, Jenni.ai is here to help. Jenni.ai is the ideal tool for helping you write your finest essay ever, thanks to its simple design, an extensive database of example essays, and cutting-edge AI technology. So, why delay? Sign up for a free trial of Jenni.ai today and begin exploring the worlds of social networking and essay writing!

Want to learn how to write an argumentative essay? Check out these inspiring examples!

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

6 Examples of Social Media Essays

Here are 6 examples of Social Media Essays:

The Impact of Social Media on Relationships and Communication

Introduction:.

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

Positive Effects:

Connecting People Across Distances

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

Improved Communication Speed and Efficiency

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

Negative Effects:

Decreased Face-to-Face Communication

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

Decreased Emotional Intimacy

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

Increased Conflict and Miscommunication

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

Conclusion:

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

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

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

The Pervasiveness of Misinformation and Fake News on Social Media

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

The Influence of Social Media on Public Opinion

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

The Challenge of Correcting Misinformation and Fake News

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

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

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

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

Comparison and Low Self-Esteem

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

Cyberbullying and Online Harassment

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

Social Isolation

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

Addiction and Overuse

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

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

The Use of Social Media in Political Activism and Social Movements

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

Raising Awareness and Mobilizing Action

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

Connecting with like-minded individuals

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

Challenges and Limitations

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

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

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

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

Data Collection and Sharing

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

Lack of Control Over Personal Information

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

Personalized Marketing

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

Government Surveillance

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

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

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

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

Effect on Individual Privacy:

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

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

Responsibility of Social Media Companies:

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

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

Better Regulation Is Needed

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

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

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

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

However, writing a comprehensive and well-researched essay on social media can be daunting. It requires a thorough understanding of the topic and the ability to articulate your ideas clearly and concisely. This is where Jenni.ai comes in. Our AI-powered tool is designed to help students like you save time and energy and focus on what truly matters - your education. With Jenni.ai , you'll have access to a wealth of examples and receive personalized writing suggestions and feedback.

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So why waste your time and energy struggling to write an essay on your own when you can have Jenni.ai by your side? Sign up for our free trial today and experience the difference for yourself! With Jenni.ai, you'll have the resources you need to write confidently, clearly, and creatively. Get started today and see just how easy and efficient writing can be!

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How to Write a Thesis Statement About Social Media

writing thesis statement about social media

Writing a thesis statement requires good research and creating a concise yet very informative point. Writing one about social media is no different. Due to the scope of the study, the information to gather and discuss is even more expansive.

What is a Social Media Thesis Statement?

Social Media Essay Outline

Social media essay titles, thesis on social media, argumentative essay on social media, social networking thesis statement, summing up the thesis statement.

Social media uses mobile technologies that are Internet-based to run communication across different parts of the world. It gives people worldwide the opportunity to communicate and socialize, unlike past means of communication which were only one-way.

The evolution of technology has made social media more efficient and prevalent than any other form of communication today. With technology’s continued evolution, social media will continue to evolve, and so will topics and thesis statements about it. A good thesis statement about social media must meet some requirements, and we will look through most of them.

What is a Social Media Thesis Statement Supposed to Look Like?

Before understanding how a thesis statement on social media should look like, we should familiarize ourselves with what thesis statements properly entail. A thesis statement is typically written in the introductory portion of a paper.

It provides an apt and rapid summary of the main point or aim of the research paper or thesis. As the name implies, it is a statement, mainly written in just one sentence.

A thesis statement briefly combines the topic and the main ideas of the paper. Usually, there are two types of thesis statements: indirect and direct. The indirect thesis statements do not mention the core areas or reason of the thesis like the direct statement does.

A direct statement mentions the main topic and discusses the reasons for the paper, while an indirect statement mentions the statement and points out three reasons for it.

For instance, an indirect social media thesis statement could go like this; “Effects of social media on youth and the reasons for its abuse.” Here the topic is clearly stated, along with the central claim of the thesis paper.

Thesis statements are created, backed up, and expatiated in the remaining parts of the paper by citing examples and bringing up other related topics that support their claim. Through this, the thesis statement then goes to help structure and develop the entire body of the writing piece.

A thesis about social media should contain a good thesis statement that would impact and organize the body of the thesis work. Thesis statements do not necessarily control the entire essay but complement it in numerous aspects.

In writing a social media essay, there is a wide variety of topics to talk about. The points are nearly endless, from information collection to technology, its impacts, and adverse effects to its evolution. Nevertheless, there is always a basic outline for an essay, and it will be structured to follow the same format.

Here is an outline for a social media essay;

Introduction

Here, you begin with the topic, state its objective, provide reasons to support its claims and finalize with a precise and accurate thesis statement.

Thesis statement

This statement should support and complement your main topic of discussion. It should provide a concise and cut-out message of the essay.

This section systematically lays out the arguments to support your topic while splitting them into paragraphs. This will gradually develop your points in a structured manner.

Each paragraph in this section must start with the topic sentence which relates directly to the thesis statement. Naturally, a paragraph should focus on one idea and be connected to the essay’s central argument.

Students must also conduct research and provide evidence to support the claims presented in the topic sentence. They can achieve this by using proper explanation methods to merge all their findings carefully.

In the conclusion of the social media essay , you restate your statement in a way that completely complements and brings all your previous arguments together. It must have a concluding paragraph that reiterates the main point discussed in the body of the content. It should also add a call to action to bring the essay into a logical closure that effortlessly lays bare all the ideas previously presented.

The social media field is continuously expanding, and there are various variations to how it can be operated and observed. Choosing a topic is easy, but choosing the right one may not be as unchallenging.

Before you begin writing an essay, the correct approach will be to review as many samples as you can. This way, you can easily understand the general concept and the adequate writing flow required to outline or develop your arguments carefully.

Picking the wrong titles can go on to make your thesis for a social media essay unnecessarily tricky to write. This can occur when you pick a topic too complex or choose one too vaguely and undervalued. This could make you get stuck when writing, so you should always pick titles that are easy to research, analyze and expand upon.

With all these in view, here are some social media essay titles;

Impact of social media on general education
Effects of using social media on businesses
Adverse effects of social media on personal relationships
The effect of government on social media and their potential restrictions
How a thesis about the effects of social media can positively impact society.

A thesis on social media should easily resemble other academic papers and concentrate on various topics in various subjects. Papers like this should take social media as their primary focus.

Keeping that in mind, a compelling social media thesis should contain specific parts like an introduction, thesis statement, body, and conclusion. Each part is essential and has its contribution and functions to the entire content of the thesis. Some students may find writing a thesis statement about social media difficult, so you can always ask our professional writers to “ write my thesis ” and we will be happy to help you.

The introduction usually contains a hook, a summary of the core points, and a concise thesis statement. The body section must carefully develop each argument and idea in a paragraph, while the conclusion should completely close all the arguments.

The tone, style, and approach to each argument should be precise and well laid out to quickly understand the general idea the thesis is trying to build upon. Depending on the level of education you are writing your thesis, you may need to conduct specific direct research on some points and be required to portray them in an encompassing manner.

Generally, thesis writing on any topic requires hard work, extensive research periods, and a good understanding of writing methods. Hence it should be approached with determination and passion. As a student in higher education, you should learn how to improve your writing skills.

An argumentative essay on social media is typically more engaging with active points of discussion and analysis. Communication is an integral aspect of human life when connecting and moving society as a whole forward. Now technology has upgraded communication to a social media age, which has become an advantage and disadvantage in many aspects of life.

An argumentative social media essay generally possesses a strong argument. The essay’s topic must be designed to prompt a person to pick a side or a discussion and provide the necessary support to back up their decision. This type of essay also requires one to research accurate facts for proper argumentative purposes.

Social media argumentative essays target the harmful effects of this brilliant innovation in communication and its uses worldwide. It is only natural as negative discussions might elicit a sense of debate and argumentation. Some examples of argumentative essay topics on social media include;

The negative effects of social media on education in different nations
Effects of social media and its impacts on the older and younger generation
How social media has taken over people
The adverse effects of social media and the digital space on our mental health
The pros and cons of social media in this society.

Social networking is an integral aspect of social media. It uses Internet-based social media sites to create connections and stay connected with friends, customers, family, and even business partners.

Social networking usually performs a primary purpose in communication with actual avenues like Twitter, Instagram, Facebook, and LinkedIn. These sites and applications enable people to connect to develop relationships and share messages, ideas, and information.

Most social networking forms entail developing and maintaining relationships using communication technology, whether it is the relationship between clients, business partners, or even students.

For example, with the development of the Internet, most students can easily find services to help write dissertations on media space, or social media marketing. All you have to do is invite me to write my dissertation and they will immediately find the best service to solve their problem.

Writing is a social networking thesis statement similar to that of a social media thesis statement. They essentially involve rational discussion, and they can be approached in the same manner. The only slight difference will be the particular attention to social media relationships. How they are developed, what it takes to maintain them, and the various merits they could provide. These would typically form the structure of a social networking thesis statement.

Writing a good thesis statement on social media involves a good understanding of the topic chosen and an accurate idea of the reasons, factors, and discussions that impact the main idea of the thesis. With all these discussed, you should be well on your way to writing good thesis statements on social media.

How should you approach your children's and teenagers' social media use?

A row of teenagers sitting down on a long bench chair, all looking at their smartphones

Children's social media usage is again in the spotlight, with the SA government announcing a proposal to ban children under 14 from accessing sites such as TikTok, Instagram and Facebook and requiring those aged 14 and 15 to have parental consent to use the apps.

But with two-thirds of primary-school-aged children and most teenagers owning their own mobile-based screen devices , is banning or restricting your child's access to social media the answer — and is it a workable solution?

'The genie is out of the bottle'

Recent research from the University of Sydney reveals Australians over the age of 14 spend an average of six hours a week on social media, and according to the eSafety Commissioner's Digital Lives of Aussie Kids report, 12–13-year-olds use an average of 3.1 social media services.

Meanjin/Brisbane-based parenting and positive psychology expert Justin Coulson says "ultimately, the social media genie is out of the bottle, and we're not getting the three wishes we hoped for".

"The great challenge that we have as parents is: how do we stuff the toothpaste back into the tube? And I just don't believe that it can be done," Dr Coulson says.

"I don't think we can make any strong arguments that [social media] has been a net positive for not just our children and youth, but for our society and for our community."

As a parent of six, including two daughters currently in their teens, Dr Coulson says ideally, he would like them "to be on social media less and use their screens less".

But the reality, he adds, is "they'll be isolated from their friends, they'll be isolated from activities that are being planned, and as much as it would be nice for their friends to send them a quick text … it's probably not going to happen because they all communicate on their various social platforms".

Setting boundaries requires trust

Some parents have opted to impose their own age restrictions on their children's social media use, including Jemma Guthrie and her partner Scott Carsdale, who recently shared how they kept their daughter off social media until she turned 15.

Ms Guthrie says restricting her daughter's social media access "wasn't a hard decision" and "came naturally based on a shared belief [with her partner] that offline life is better for children".

"I think it's hard to do if you haven't already established a culture of limit-setting in your own household," she says.

Dr Coulson says in order to establish boundaries and set limits "there's got to be a foundation of trust".

"My definition of trust is really simple; it's believing the other person is going to act in your best interests.

"So if you say 'no social media until 16 or 18' and [your children] don't believe that that's in their best interest, they don't believe you're going to act in their best interests, then no matter what you do, you're going to be diminishing yourself in their eyes and reducing your influence."

He says one of the primary roles of parenting is to socialise children and teach them values and morality, both online and off.

"The rules around social media are exactly the same as the rules around living a good life: There are rules around respect, consent, kindness, and support.

"Because if we're raising good kids, they'll be good kids, whether they're online or offline."

University of Sydney Media and Communications lecturer Catherine Page Jeffery specialises in research on parenting in the digital age, and says there is no simple fix.

"The problem is if you say 'no, you're not having it at all', and then they just go behind your back … they're not going to come to you if they have any problems or experience any difficulties in those spaces."

Is there a 'right age' to join social media?

Dr Page Jeffery says while social media may negatively affect some young people's wellbeing, that's not always the case.

"There is no magic age at which young people suddenly are bestowed with all of the skills and competencies to effectively navigate social media."

She explains children develop at different rates and have different levels of maturity, and some younger people "are much more sensible and risk-averse than others".

"Bearing that in mind, parents should really make their own judgement about when their child should or might be allowed to go on to social media.

"Obviously that depends on the age of the child, you probably wouldn't let a six- or seven-year-old [on social media apps] unseen and unsupervised, but certainly with older children, I think giving them some agency but providing support is not a bad approach."

The difference between risk and harm

Dr Page Jeffery acknowledges there is a steady stream of media reporting about research into the potentially adverse effects of social media on children and teenagers — including links between social media use and poor mental health and low self-esteem — but says those studies "often don't show causation".

"Parents hear about studies … and then their kids want to get on [social media] and their kids say, 'Look, I use it, and it's good for me, and this is what I get out of it', so parents are really conflicted."

A young girl of Asian heritage is on a bed, looking at a smartphone

"It's really hard, and you know what? I think letting your kids go and explore online spaces is not as bad as it sounds, as long as you can put some parameters and guidelines in place."

She accepts there are very real risks and says "of course, there needs to be certain mechanisms to address those risks", but warns it is important not to conflate risk with harm.

"Exposure to some risk and navigating risk is a really important part of young people's development.

"It teaches them the sort of skills they need to safely manage online spaces, and also helps them develop resilience."

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Is social media affecting your mental health? There are ways you can change that

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What parents worry about and what teens say concern them are worlds apart

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Families and schools share responsibility in developing children’s digital wellness

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Front Psychol

The effect of social media on the development of students’ affective variables

1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China

2 School of Marxism, Hohai University, Nanjing, Jiangsu, China

3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Published: 18 May 2024

Emotions unveiled: detecting COVID-19 fake news on social media

Bahareh Farhoudinia ORCID: orcid.org/0000-0002-2294-8885 1 ,
Selcen Ozturkcan ORCID: orcid.org/0000-0003-2248-0802 1 , 2 &
Nihat Kasap ORCID: orcid.org/0000-0001-5435-6633 1

Humanities and Social Sciences Communications volume 11 , Article number: 640 ( 2024 ) Cite this article

Metrics details

Business and management
Science, technology and society

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.

Introduction

Social media has changed human life in multiple ways. People from all around the world are connected via social media. Seeking information, entertainment, communicatory utility, convenience utility, expressing opinions, and sharing information are some of the gratifications of social media (Whiting and Williams, 2013 ). Social media is also beneficial for political parties or companies since they can better connect with their audience through social media (Kumar et al., 2016 ). Despite all the benefits that social media adds to our lives, there are also disadvantages to its use. The emergence of fake news is one of the most important and dangerous consequences of social media (Baccarella et al., 2018 , 2020 ). Zhou et al. ( 2019 ) suggested that fake news threatens public trust, democracy, justice, freedom of expression, and the economy. In the 2016 United States (US) presidential election, fake news engagement outperformed mainstream news engagement and significantly impacted the election results (Silverman, 2016 ). In addition to political issues, fake news can cause irrecoverable damage to companies. For instance, Pepsi stock fell by 4% in 2016 when a fake story about the company’s CEO spread on social media (Berthon and Pitt, 2018 ). During the COVID-19 pandemic, fake news caused serious problems, e.g., people in Europe burned 5G towers because of a rumor claiming that these towers damaged the immune system of humans (Mourad et al., 2020 ). The World Health Organization (WHO) asserted that misinformation and propaganda propagated more rapidly than the COVID-19 pandemic, leading to psychological panic, the circulation of misleading medical advice, and an economic crisis.

This study, which is a part of a completed PhD thesis (Farhoundinia, 2023 ), focuses on analyzing the emotions and sentiments elicited by fake news in the context of COVID-19. The purpose of this paper is to investigate how emotions can help detect fake news. This study aims to address the following research questions: 1. How do the sentiments associated with real news and fake news differ? 2. How do the emotions elicited by fake news differ from those elicited by real news? 3. What particular emotions are most prevalent in fake news? 4. How can these feelings be used to recognize fake news on social media?

This paper is arranged into six sections: Section “Related studies” reviews the related studies; Section “Methods” explains the proposed methodology; and Section “Results and analysis” presents the implemented models, analysis, and related results in detail. Section “Discussion and limitations” discusses the research limitations, and the conclusion of the study is presented in Section “Conclusion”.

Related studies

Research in the field of fake news began following the 2016 US election (Carlson, 2020 ; Wang et al., 2019 ). Fake news has been a popular topic in multiple disciplines, such as journalism, psychology, marketing, management, health care, political science, information science, and computer science (Farhoudinia et al., 2023 ). Therefore, fake news has not been defined in a single way; according to Berthon and Pitt ( 2018 ), misinformation is the term used to describe the unintentional spread of fake news. Disinformation is the term used to describe the intentional spread of fake news to mislead people or attack an idea, a person, or a company (Allcott and Gentzkow, 2017 ). Digital assets such as images and videos could be used to spread fake news (Rajamma et al., 2019 ). Advancements in computer graphics, computer vision, and machine learning have made it feasible to create fake images or movies by merging them together (Agarwal et al., 2020 ). Additionally, deep fake videos pose a risk to public figures, businesses, and individuals in the media. Detecting deep fakes is challenging, if not impossible, for humans.

The reasons for believing and sharing fake news have attracted the attention of several researchers (e.g., Al-Rawi et al., 2019 ; Apuke and Omar, 2020 ; Talwar, Dhir et al., 2019 ). Studies have shown that people have a tendency to favor news that reinforces their existing beliefs, a cognitive phenomenon known as confirmation bias. This inclination can lead individuals to embrace misinformation that aligns with their preconceived notions (Kim and Dennis, 2019 ; Meel and Vishwakarma, 2020 ). Although earlier research focused significantly on the factors that lead people to believe and spread fake news, it is equally important to understand the cognitive mechanisms involved in this process. These cognitive mechanisms, as proposed by Kahneman ( 2011 ), center on two distinct systems of thinking. In system-one cognition, conclusions are made without deep or conscious thoughts; however, in system-two cognition, there is a deeper analysis before decisions are made. Based on Moravec et al. ( 2020 ), social media users evaluate news using ‘system-one’ cognition; therefore, they believe and share fake news without deep thinking. It is essential to delve deeper into the structural aspects of social media platforms that enable the rapid spread of fake news. Social media platforms are structured to show that posts and news are aligned with users’ ideas and beliefs, which is known as the root cause of the echo chamber effect (Cinelli et al., 2021 ). The echo chamber effect has been introduced as an aspect that causes people to believe and share fake news on social media (e.g., Allcott and Gentzkow, 2017 ; Berthon and Pitt, 2018 ; Chua and Banerjee, 2018 ; Peterson, 2019 ).

In the context of our study, we emphasize the existing body of research that specifically addresses the detection of fake news (Al-Rawi et al., 2019 ; Faustini and Covões, 2020 ; Ozbay and Alatas, 2020 ; Raza and Ding, 2022 ). Numerous studies that are closely aligned with the themes of our present investigation have delved into methodological approaches for identifying fake news (Er and Yılmaz, 2023 ; Hamed et al., 2023 ; Iwendi et al., 2022 ). Fake news detection methods are classified into three categories: (i) content-based, (ii) social context, and (iii) propagation-based methods. (i) Content-based fake news detection models are based on the content and linguistic features of the news rather than user and propagation characteristics (Zhou and Zafarani, 2019 , p. 49). (ii) Fake news detection based on social context employs user demographics such as age, gender, education, and follower–followee relationships of the fake news publishers as features to recognize fake news (Jarrahi and Safari, 2023 ). (iii) Propagation-based approaches are based on the spread of news on social media. The input of the propagation-based fake news detection model is a cascade of news, not text or user profiles. Cascade size, cascade depth, cascade breadth, and node degree are common features of detection models (Giglietto et al., 2019 ; de Regt et al., 2020 ; Vosoughi et al., 2018 ).

Machine learning methods are widely used in the literature because they enable researchers to handle and process large datasets (Ongsulee, 2017 ). The use of machine learning in fake news research has been extremely beneficial, especially in the domains of content-based, social context-based, and propagation-based fake news identification. These methods leverage the advantages of a range of characteristics, including sentiment-related, propagation, temporal, visual, linguistic, and user/account aspects. Fake news detection frequently makes use of machine learning techniques such as logistic regressions, decision trees, random forests, naïve Bayes, and support vector machine (SVM). Studies on the identification of fake news also include deep learning models, such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks, which can provide better accuracy in certain situations. Even with a small amount of training data, pretrained language models such as bidirectional encoder representations from transformers (BERT) show potential for identifying fake news (Kaliyar et al., 2021 ). Amer et al. ( 2022 ) investigated the usefulness of these models in benchmark studies covering different topics.

The role of emotions in identifying fake news within academic communities remains an area with considerable potential for additional research. Despite many theoretical and empirical studies, this topic remains inadequately investigated. Ainapure et al. ( 2023 ) analyzed the sentiments elicited by tweets in India during the COVID-19 pandemic with deep learning and lexicon-based techniques using the valence-aware dictionary and sentiment reasoner (Vader) and National Research Council (NRC) lexicons to understand the public’s concerns. Dey et al. ( 2018 ) applied several natural language processing (NLP) methods, such as sentiment analysis, to a dataset of tweets about the 2016 U.S. presidential election. They found that fake news had a strong tendency toward negative sentiment; however, their dataset was too limited (200 tweets) to provide a general understanding. Cui et al. ( 2019 ) found that sentiment analysis was the best-performing component in their fake news detection framework. Ajao et al. ( 2019 ) studied the hypothesis that a relationship exists between fake news and the sentiments elicited by such news. The authors tested hypotheses with different machine learning classifiers. The best results were obtained by sentiment-aware classifiers. Pennycook and Rand ( 2020 ) argued that reasoning and analytical thinking help uncover news credibility; therefore, individuals who engage in reasoning are less likely to believe fake news. Prior psychology research suggests that an increase in the use of reason implies a decrease in the use of emotions (Mercer, 2010 ).

In this study, we apply sentiment analysis to the more general topic of fake news detection. The focus of this study is on the tweets that were shared during the COVID-19 pandemic. Many scholars focused on the effects of media reports, providing comprehensive information and explanations about the virus. However, there is still a gap in the literature on the characteristics and spread of fake news during the COVID-19 pandemic. A comprehensive study can enhance preparedness efforts for any similar future crisis. The aim of this study is to answer the question of how emotions aid in fake news detection during the COVID-19 pandemic. Our hypothesis is that fake news carries negative emotions and is written with different emotions and sentiments than those of real news. We expect to extract more negative sentiments and emotions from fake news than from real news. Existing works on fake news detection have focused mainly on news content and social context. However, emotional information has been underutilized in previous studies (Ajao et al., 2019 ). We extract sentiments and eight basic emotions from every tweet in the COVID-19 Twitter dataset and use these features to classify fake and real news. The results indicate how emotions can be used in differentiating and detecting fake and real news.

With our methodology, we employed a multifaceted approach to analyze tweet text and discern sentiment and emotion. The steps involved were as follows: (a) Lexicons such as Vader, TextBlob, and SentiWordNet were used to identify sentiments embedded in the tweet content. (b) The NRC emotion lexicon was utilized to recognize the range of different emotions expressed in the tweets. (c) Machine learning models, including the random forest, naïve Bayes, and SVM classifiers, as well as a deep learning model, BERT, were integrated. These models were strategically applied to the data for fake news detection, both with and without considering emotions. This comprehensive approach allowed us to capture nuanced patterns and dependencies within the tweet data, contributing to a more effective and nuanced analysis of the fake news content on social media.

An open, science-based, publicly available dataset was utilized. The dataset comprises 10,700 English tweets with hashtags relevant to COVID-19, categorized with real and fake labels. Previously used by Vasist and Sebastian ( 2022 ) and Suter et al. ( 2022 ), the manually annotated dataset was compiled by Patwa et al. ( 2021 ) in September 2020 and includes tweets posted in August and September 2020. According to their classification, the dataset is balanced, with 5600 real news stories and 5100 fake news stories. The dataset used for the study was generated by sourcing fake news data from public fact-checking websites and social media outlets, with manual verification against the original documents. Web-based resources, including social media posts and fact-checking websites such as PolitiFact and Snopes, played a key role in collecting and adjudicating details on the veracity of claims related to COVID-19. For real news, tweets from official and verified sources were gathered, and each tweet was assessed by human reviewers based on its contribution of relevant information about COVID-19 (Patwa et al., 2021 ; Table 2 on p. 4 of Suter et al., 2022 , which is excerpted from Patwa et al. ( 2021 ), also provides an illustrative overview).

Preprocessing is an essential step in any data analysis, especially when dealing with textual data. Appropriate preprocessing steps can significantly enhance the performance of the models. The following preprocessing steps were applied to the dataset: removing any characters other than alphabets, change the letters to lower-case, deleting stop words such as “a,” “the,” “is,” and “are,” which carry very little helpful information, and performing lemmatization. The text data were transformed into quantitative data by the scikit-learn ordinal encoder class.

The stages involved in this research are depicted in a high-level schematic that is shown in Fig. 1 . First, the sentiments and emotions elicited by the tweets were extracted, and then, after studying the differences between fake and real news in terms of sentiments and emotions, these characteristics were utilized to construct fake news detection models.

The figure depicts the stages involved in this research in a high-level schematic.

Sentiment analysis

Sentiment analysis is the process of deriving the sentiment of a piece of text from its content (Vinodhini and Chandrasekaran, 2012 ). Sentiment analysis, as a subfield of natural language processing, is widely used in analyzing the reviews of a product or service and social media posts related to different topics, events, products, or companies (Wankhade et al., 2022 ). One major application of sentiment analysis is in strategic marketing. Păvăloaia et al. ( 2019 ), in a comprehensive study on two companies, Coca-Cola and PepsiCo, confirmed that the activity of these two brands on social media has an emotional impact on existing or future customers and the emotional reactions of customers on social media can influence purchasing decisions. There are two methods for sentiment analysis: lexicon-based and machine-learning methods. Lexicon-based sentiment analysis uses a collection of known sentiments that can be divided into dictionary-based lexicons or corpus-based lexicons (Pawar et al., 2015 ). These lexicons help researchers derive the sentiments generated from a text document. Numerous dictionaries, such as Vader (Hutto and Gilbert, 2014 ), SentiWordNet (Esuli and Sebastiani, 2006 ), and TextBlob (Loria, 2018 ), can be used for scholarly research.

In this research, Vader, TextBlob, and SentiWordNet are the three lexicons used to extract the sentiments generated from tweets. The Vader lexicon is an open-source lexicon attuned specifically to social media (Hutto and Gilbert, 2014 ). TextBlob is a Python library that processes text specifically designed for natural language analysis (Loria, 2018 ), and SentiWordNet is an opinion lexicon adapted from the WordNet database (Esuli and Sebastiani, 2006 ). Figure 2 shows the steps for the sentiment analysis of tweets.

The figure illustrates the steps for the sentiment analysis of tweets.

Different methods and steps were used to choose the best lexicon. First, a random partition of the dataset was manually labeled as positive, negative, or neutral. The results of every lexicon were compared with the manually labeled sentiments, and the performance metrics for every lexicon are reported in Table 1 . Second, assuming that misclassifying negative and positive tweets as neutral is not as crucial as misclassifying negative tweets as classifying positive tweets, the neutral tweets were ignored, and a comparison was made on only positive and negative tweets. The three-class and two-class classification metrics are compared in Table 1 .

Third, this study’s primary goal was to identify the precise distinctions between fake and real tweets to improve the detection algorithm. We addressed how well fake news was detected with the three sentiment lexicons, as different results were obtained. This finding means that a fake news detection model was trained with the dataset using the outputs from three lexicons: Vader, TextBlob, and SentiWordNet. As previously indicated, the dataset includes labels for fake and real news, which allows for the application of supervised machine learning detection models and the evaluation of how well various models performed. The Random Forest algorithm is a supervised machine learning method that has achieved good performance in the classification of text data. The dataset contains many tweets and numerical data reporting the numbers of hospitalized, deceased, and recovered individuals who do not carry any sentiment. During this phase, tweets containing numerical data were excluded; this portion of the tweets constituted 20% of the total. Table 2 provides information on the classification power using the three lexicons with nonnumerical data. The models were more accurate when using sentiments drawn from Vader. This finding means the Vader lexicon may include better classifications of fake and real news. Vader was selected as the superior sentiment lexicon after evaluating all three processes. The steps for choosing the best lexicon are presented in Fig. 3 (also see Appendix A in Supplementary Information for further details on the procedure). Based on the results achieved when using Vader, the tweets that are labeled as fake include more negative sentiments than those of real tweets. Conversely, real tweets include more positive sentiments.

The figure exhibits the steps for choosing the best lexicon.

Emotion extraction

Emotions elicited in tweets were extracted using the NRC emotion lexicon. This lexicon measures emotional effects from a body of text, contains ~27,000 words, and is based on the National Research Council Canada’s affect lexicon and the natural language toolkit (NLTK) library’s WordNet synonym sets (Mohammad and Turney, 2013 ). The lexicon includes eight scores for eight emotions based on Plutchick’s model of emotion (Plutchik, 1980 ): joy, trust, fear, surprise, sadness, anticipation, anger, and disgust. These emotions can be classified into four opposing pairs: joy–sadness, anger–fear, trust–disgust, and anticipation–surprise. The NRC lexicon assigns each text the emotion with the highest score. Emotion scores from the NRC lexicon for every tweet in the dataset were extracted and used as features for the fake news detection model. The features of the model include the text of the tweet, sentiment, and eight emotions. The model was trained with 80% of the data and tested with 20%. Fake news had a greater prevalence of negative emotions, such as fear, disgust, and anger, than did real news, and real news had a greater prevalence of positive emotions, such as anticipation, joy, and surprise, than did fake news.

Fake news detection

In the present study, the dataset was divided into a training set (80%) and a test set (20%). The dataset was analyzed using three machine learning models: random forest, SVM, and naïve Bayes. Appendices A and B provide information on how the results were obtained and how they correlate with the research corpus.

Random forest : An ensemble learning approach that fits several decision trees to random data subsets. This classifier is popular for text classification, high-dimensional data, and feature importance since it overfits less than decision trees. The Random Forest classifier in scikit-learn was used in this study (Breiman, 2001 ).

Naïve Bayes : This model uses Bayes’ theorem to solve classification problems, such as sorting documents into groups and blocking spam. This approach works well with text data and is easy to use, strong, and good for problems with more than one label. The Naïve Bayes classifier from scikit-learn was used in this study (Zhang, 2004 ).

Support vector machines (SVMs) : Supervised learning methods that are used to find outliers, classify data, and perform regression. These methods work well with data involving many dimensions. SVMs find the best hyperplanes for dividing classes. In this study, the SVM model from scikit-learn was used (Cortes and Vapnik, 1995 ).

Deep learning models can learn how to automatically describe data in a hierarchical way, making them useful for tasks such as identifying fake news (Salakhutdinov et al., 2012 ). A language model named bidirectional encoder representations from transformers (BERT) was used in this study to help discover fake news more easily.

BERT : A cutting-edge NLP model that uses deep neural networks and bidirectional learning and can distinguish patterns on both sides of a word in a sentence, which helps it understand the context and meaning of text. BERT has been pretrained with large datasets and can be fine-tuned for specific applications to capture unique data patterns and contexts (Devlin et al., 2018 ).

In summary, we applied machine learning models (random forest, naïve Bayes, and SVM) and a deep learning model (BERT) to analyze text data for fake news detection. The impact of emotion features on detecting fake news was compared between models that include these features and models that do not include these features. We found that adding emotion scores as features to machine learning and deep learning models for fake news detection can improve the model’s accuracy. A more detailed analysis of the results is given in the section “Results and analysis”.

Results and analysis

In the sentiment analysis using tweets from the dataset, positive and negative sentiment tweets were categorized into two classes: fake and real. Figure 4 shows a visual representation of the differences, while the percentages of the included categories are presented in Table 3 . In fake news, the number of negative sentiments is greater than the number of positive sentiments (39.31% vs. 31.15%), confirming our initial hypothesis that fake news disseminators use extreme negative emotions to attract readers’ attention.

The figure displays a visual representation of the differences of sentiments in each class.

Fake news disseminators aim to attack or satirize an idea, a person, or a brand using negative words and emotions. Baumeister et al. ( 2001 ) suggested that negative events are stronger than positive events and that negative events have a more significant impact on individuals than positive events. Accordingly, individuals sharing fake news tend to express more negativity for increased impressiveness. The specific topics of the COVID-19 pandemic, such as the source of the virus, the cure for the illness, the strategy the government is using against the spread of the virus, and the spread of vaccines, are controversial topics. These topics, known for their resilience against strong opposition, have become targets of fake news featuring negative sentiments (Frenkel et al., 2020 ; Pennycook et al., 2020 ). In real news, the pattern is reversed, and positive sentiments are much more frequent than negative sentiments (46.45% vs. 35.20%). Considering that real news is spread among reliable news channels, we can conclude that reliable news channels express news with positive sentiments so as not to hurt their audience psychologically and mentally.

The eight scores for the eight emotions of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust were extracted from the NRC emotion lexicon for every tweet. Each text was assigned the emotion with the highest score. Table 4 and Fig. 5 include more detailed information about the emotion distribution.

The figure depicts more detailed information about the emotion distribution.

The NRC lexicon provides scores for each emotion. Therefore, the intensities of emotions can also be compared. Table 5 shows the average score of each emotion for the two classes, fake and real news.

A two-sample t -test was performed using the pingouin (PyPI) statistical package in Python (Vallat, 2018 ) to determine whether the difference between the two groups was significant (Tables 6 and 7 ).

As shown in Table 6 , the P values indicate that the differences in fear, anger, trust, surprise, disgust, and anticipation were significant; however, for sadness and joy, the difference between the two groups of fake and real news was not significant. Considering the statistics provided in Tables 4 , 5 , and Fig. 5 , the following conclusions can be drawn:

Anger, disgust, and fear are more commonly elicited in fake news than in real news.

Anticipation and surprise are more commonly elicited in real news than in fake news.

Fear is the most commonly elicited emotion elicited in both fake and real news.

Trust is the second most commonly elicited emotion in fake and real news.

The most significant differences were observed for trust, fear, and anticipation (5.92%, 5.33%, and 3.05%, respectively). The differences between fake and real news in terms of joy and sadness were not significant.

In terms of intensity, based on Table 5 ,

Fear is the mainly elicited emotion in both fake and real news; however, fake news has a higher fear intensity score than does real news.

Trust is the second most commonly elicited emotion in two categories—real and fake—but is more powerful in real news.

Positive emotions, such as anticipation, surprise, and trust, are more strongly elicited in real news than in fake news.

Anger, disgust, and fear are among the stronger emotions elicited by fake news. Joy and sadness are elicited in both classes almost equally.

During the COVID-19 pandemic, fake news disseminators seized the opportunity to create fearful messages aligned with their objectives. The existence of fear in real news is also not surprising because of the extraordinary circumstances of the pandemic. The most crucial point of the analysis is the significant presence of negative emotions elicited by fake news. This observation confirms our hypothesis that fake news elicits extremely negative emotions. Positive emotions such as anticipation, joy, and surprise are elicited more often in real news than in fake news, which also aligns with our hypothesis. The largest differences in elicited emotions are as follows: trust, fear, and anticipation.

We used nine features for every tweet in the dataset: sentiment and eight scores for every emotion and sentiment in every tweet. These features were utilized for supervised machine learning fake news detection models. A schematic explanation of the models is given in Fig. 6 . The dataset was divided into training and test sets, with an 80%–20% split. The scikit-learn random forest, SVM, and Naïve Bayes machine learning models with default hyperparameters were implemented using emotion features to detect fake news in nonnumerical data. Then, we compared the prediction power of the models with that of models without these features. The performance metrics of the models, such as accuracy, precision, recall, and F1-score, are given in Table 7 .

The figure exhibits a schematic explanation of the model.

When joy and sadness were removed from the models, the accuracy decreased. Thus, the models performed better when all the features were included (see Table C.1. Feature correlation scores in Supplementary Information). The results confirmed that elicited emotions can help identify fake and real news. Adding emotion features to the detection models significantly increased the performance metrics. Figure 7 presents the importance of the emotion features used in the random forest model.

The figure illustrates the importance of the emotion features used in the Random Forest model.

In the random forest classifier, the predominant attributes were anticipation, trust, and fear. The difference in the emotion distribution between the two classes of fake and real news was also more considerable for anticipation, trust, and fear. It can be claimed that fear, trust, and anticipation emotions have good differentiating power between fake and real news.

BERT was the other model that was employed for the task of fake news detection using emotion features. The BERT model includes a number of preprocessing stages. The text input is segmented using the BERT tokenizer, with sequence truncation and padding ensuring that the length does not exceed 128 tokens, a reduction from the usual 512 tokens due to constraints on computing resources. The optimization process utilized the AdamW optimizer with a set learning rate of 0.00001. To ascertain the best number of training cycles, a 5-fold cross-validation method was applied, which established that three epochs were optimal. The training phase consisted of three unique epochs. The model was executed on Google Colab using Python, a popular programming language. The model was evaluated with the test set after training. Table 8 shows the performance of the BERT model with and without using emotions as features.

The results indicate that adding emotion features had a positive impact on the performance of the random forest, SVM, and BERT models; however, the naïve Bayes model achieved better performance without adding emotion features.

Discussion and limitations

This research makes a substantial impact on the domain of detecting fake news. The goal was to explore the range of sentiments and emotional responses linked to both real and fake news in pursuit of fulfilling the research aims and addressing the posed inquiries. By identifying the emotions provoked as key indicators of fake news, this study adds valuable insights to the existing corpus of related scholarly work.

Our research revealed that fake news triggers a higher incidence of negative emotions compared to real news. Sentiment analysis indicated that creators of fake news on social media platforms tend to invoke more negative sentiments than positive ones, whereas real news generally elicits more positive sentiments than negative ones. We extracted eight emotions—anger, anticipation, disgust, fear, joy, sadness, surprise, and trust—from each tweet analyzed. Negative and potent emotions such as fear, disgust, and anger were more frequently found elicited in fake news, in contrast to real news, which was more likely to arouse lighter and positive emotions such as anticipation, joy, and surprise. The difference in emotional response extended beyond the range of emotions to their intensity, with negative feelings like fear, anger, and disgust being more pronounced in fake news. We suggest that the inclusion of emotional analysis in the development of automated fake news detection algorithms could improve the effectiveness of the machine learning and deep learning models designed for fake news detection in this study.

Due to negativity bias (Baumeister et al., 2001 ), bad news, emotions, and feedback tend to have a more outsized influence than positive experiences. This suggests that humans are more likely to assign greater weight to negative events over positive ones (Lewicka et al., 1992 ). Our findings indicate that similar effects are included in social media user behavior, such as sharing and retweeting. Furthermore, the addition of emotional features to the fake news detection models was found to improve their performance, providing an opportunity to investigate their moderating effects on fake news dissemination in future research.

The majority of the current research on identifying fake news involves analyzing the social environment and news content (Amer et al., 2022 ; Jarrahi and Safari, 2023 ; Raza and Ding, 2022 ). Despite its possible importance, the investigation of emotional data has not received sufficient attention in the past (Ajao et al., 2019 ). Although sentiment in fake news has been studied in the literature, earlier studies mostly neglected a detailed examination of certain emotions. Dey et al. ( 2018 ) contributed to this field by revealing a general tendency toward negativity in fake news. Their results support our research and offer evidence for the persistent predominance of negative emotions elicited by fake news. Dey et al. ( 2018 ) also found that trustworthy tweets, on the other hand, tended to be neutral or positive in sentiment, highlighting the significance of sentiment polarity in identifying trustworthy information.

Expanding upon this sentiment-focused perspective, Cui et al. ( 2019 ) observed a significant disparity in the sentiment polarity of comments on fake news as opposed to real news. Their research emphasized the clear emotional undertones in user reactions to false material, highlighting the importance of elicited emotions in the context of fake news. Similarly, Dai et al. ( 2020 ) analyzed false health news and revealed a tendency for social media replies to real news to be marked by a more upbeat tone. These comparative findings highlight how elicited emotions play a complex role in influencing how people engage with real and fake news.

Our analysis revealed that the emotions conveyed in fake tweets during the COVID-19 pandemic are in line with the more general trends found in other studies on fake news. However, our research extends beyond that of current studies by offering detailed insights into the precise distribution and strength of emotions elicited by fake tweets. This detailed research closes a significant gap in the body of literature by adding a fresh perspective on our knowledge of emotional dynamics in the context of disseminating false information. Our research contributes significantly to the current discussion on fake news identification by highlighting these comparative aspects and illuminating both recurring themes and previously undiscovered aspects of emotional data in the age of misleading information.

The present analysis was performed with a COVID-19 Twitter dataset, which does not cover the whole period of the pandemic. A complementary study on a dataset that covers a wider time interval might yield more generalizable findings, while our study represents a new effort in the field. In this research, the elicited emotions of fake and real news were compared, and the emotion with the highest score was assigned to each tweet, while an alternative method could be to compare the emotion score intervals for fake and real news. The performance of detection models could be further improved by using pretrained emotion models and adding additional emotion features to the models. In a future study, our hypothesis that “fake news and real news are different in terms of elicited emotions, and fake news elicits more negative emotions” could be examined in an experimental field study. Additionally, the premises and suppositions underlying this study could be tested in emergency scenarios beyond the COVID-19 context to enhance the breadth of crisis readiness.

The field of fake news research is interdisciplinary, drawing on the expertise of scholars from various domains who can contribute significantly by formulating pertinent research questions. Psychologists and social scientists have the opportunity to delve into the motivations and objectives behind the creators of fake news. Scholars in management can offer strategic insights for organizations to deploy in countering the spread of fake news. Legislators are in a position to draft laws that effectively stem the flow of fake news across social media channels. In addition, the combined efforts of researchers from other academic backgrounds can make substantial additions to the existing literature on fake news.

The aim of this research was to propose novel attributes for current fake news identification techniques and to explore the emotional and sentiment distinctions between fake news and real news. This study was designed to tackle the subsequent research questions: 1. How do the sentiments associated with real news and fake news differ? 2. How do the emotions elicited by fake news differ from those elicited by real news? 3. What particular elicited emotions are most prevalent in fake news? 4. How could these elicited emotions be used to recognize fake news on social media? To answer these research questions, we thoroughly examined tweets related to COVID-19. We employed a comprehensive strategy, integrating lexicons such as Vader, TextBlob, and SentiWordNet together with machine learning models, including random forest, naïve Bayes, and SVM, as well as a deep learning model named BERT. We first performed sentiment analysis using the lexicons. Fake news elicited more negative sentiments, supporting the idea that disseminators use extreme negativity to attract attention. Real news elicited more positive sentiments, as expected from trustworthy news channels. For fake news, there was a greater prevalence of negative emotions, including fear, disgust, and anger, while for real news, there was a greater frequency of positive emotions, such as anticipation, joy, and surprise. The intensity of these emotions further differentiated fake and real news, with fear being the most dominant emotion in both categories. We applied machine learning models (random forest, naïve Bayes, SVM) and a deep learning model (BERT) to detect fake news using sentiment and emotion features. The models demonstrated improved accuracy when incorporating emotion features. Anticipation, trust, and fear emerged as significant differentiators between fake and real news, according to the random forest feature importance analysis.

The findings of this research could lead to reliable resources for communicators, managers, marketers, psychologists, sociologists, and crisis and social media researchers to further explain social media behavior and contribute to the existing fake news detection approaches. The main contribution of this study is the introduction of emotions as a role-playing feature in fake news detection and the explanation of how specific elicited emotions differ between fake and real news. The elicited emotions extracted from social media during a crisis such as the COVID-19 pandemic could not only be an important variable for detecting fake news but also provide a general overview of the dominant emotions among individuals and the mental health of society during such a crisis. Investigating and extracting further features of fake news has the potential to improve the identification of fake news and may allow for the implementation of preventive measures. Furthermore, the suggested methodology could be applied to detecting fake news in fields such as politics, sports, and advertising. We expect to observe a similar impact of emotions on other topics as well.

Data availability

The datasets analyzed during the current study are available in the Zenodo repository: https://doi.org/10.5281/zenodo.10951346 .

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Bahareh Farhoudinia, Selcen Ozturkcan & Nihat Kasap

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Bahareh Farhoudinia (first author) conducted the research, retrieved the open access data collected by other researchers, conducted the analysis, and drafted the manuscript as part of her PhD thesis successfully completed at Sabancı University in the year 2023. Selcen Ozturkcan (second author and PhD co-advisor) provided extensive guidance throughout the research process, co-wrote sections of the manuscript, and offered critical feedback on the manuscript. Nihat Kasap (third author and PhD main advisor) oversaw the overall project and provided valuable feedback on the manuscript.

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Farhoudinia, B., Ozturkcan, S. & Kasap, N. Emotions unveiled: detecting COVID-19 fake news on social media. Humanit Soc Sci Commun 11 , 640 (2024). https://doi.org/10.1057/s41599-024-03083-5

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DOI : https://doi.org/10.1057/s41599-024-03083-5

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Morgan Meaker

Meta Faces Fresh Probe Over ‘Addictive’ Effect on Kids

The European Union has opened an investigation into Facebook and Instagram for the platforms’ potentially addictive effects on children, echoing two similar probes opened into TikTok earlier this year .

Meta-owned platforms will be investigated for their addictive and “rabbit hole” effects, and whether young users were being fed too much content about depression or unrealistic body images. Investigators will also probe whether underage children below 13 years old are being effectively blocked from using the services.

“We are not convinced that Meta has done enough to comply with the DSA [Digital Services Act] obligations—to mitigate the risks of negative effects to the physical and mental health of young Europeans on its platforms Facebook and Instagram,” Thierry Breton, the EU’s internal markets commissioner who is leading the investigations, said on X.

“We want young people to have safe, age-appropriate experiences online,” said Meta spokesperson Kirstin MacLeod, adding the company has developed more than 50 tools and policies designed to protect young people. “This is a challenge the whole industry is facing, and we look forward to sharing details of our work with the European Commission.”

The investigations into Meta and TikTok under the bloc’s new Digital Services Act rules were separate, a commission spokesperson said, adding that similarities between the cases simply reflected resemblances in how the platforms work. “There are some competitive effects in the markets where some platforms copy other platforms’ features,” they said.

The effects of social media on children has sparked intense debate in recent months, following the publication of the book The Anxious Generation by Jonathan Haidt. The NYU social psychologist argues that the prevalence of social media use among young people is rewiring children’s brains and making them more anxious. In October, a coalition of US states sued Meta , alleging the company’s products are harmful to children’s mental health.

The Digital Services Act is an expansive rulebook that aims to protect Europeans’ human rights online and took effect for the largest platforms in August last year. So far, the EU has investigations open into six platforms for different reasons: AliExpress, Facebook, Instagram, TikTok, TikTok Lite, and X. Under the Digital Services Act, platforms can be fined up to 6 percent of their global revenue.

After the EU launched an investigation into a points-for-views reward system on TikTok Lite—a version of the app which uses less data—the company said it would suspend the incentive following concerns about its impact on children.

“Our children are not guinea pigs for social media,” Breton said at the time.

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negative emotions seem to indicate that addictive behaviors are on the rise and are closely related. to overuse of social media. On the other side of the coin, social media use also produces positive effects on. emotional well-being such as happiness, Mudita, humor, support, validation, and a more frequent.
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Social media platforms can be hotspots for cyberbullying and spreading hurtful rumors, lies, and abuse that can leave lasting emotional scars. Ways to safely use social media. A great way to improve your relationship with social media and help decrease the negative effects is to decrease your daily screen time. Tips to decrease screen time include:
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ORIGINAL RESEARCH article

Introduction

Literature Review

Social Media Use, Social Capital, and Psychological Well-Being

Social Media Use, Social Isolation, and Psychological Well-Being

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Indirect Relationship Between Social Media Use and Psychological Well-Being

Research Methodology

Data Collection Procedures and Respondent's Information

Measurement Items

Social Media Use

Social Capital

Social Isolation

Smartphone Addiction

Psychological Well-Being

Data Analysis

Common Method Bias (CMB) Test

Assessment of Measurement Model

Assessment of the Structural Model

Theoretical Contributions

Practical Contributions

Limitations and Directions for Future Studies

Data Availability Statement

Ethics Statement

Author Contributions

Conflict of Interest

Pros & cons: impacts of social media on mental health

Data Availability

Acknowledgements

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Social Media Use and Impact on Interpersonal Communication

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1 Introduction

2 Related Work

3 Experimental Design

4 Data Collection

4.1 Social Interaction Changes

5 Discussion

6 Conclusion

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Thesis Statements about Social Media: 21 Examples and Tips

How to Write a Thesis Statement?

Start With a Question and Develop an Answer

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Make the Argument Clear

Question your Assumptions

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