How Heavy Use of Social Media Is Linked to Mental Illness Research Paper

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Introduction

Background

At no point in history was the world as interconnected as it is now. The Internet allows people from far-flung corners of the world to communicate at an instantaneous speed. Social networks unite hundreds of millions of people on such platforms as Instagram, Facebook, and Twitter. People of all social statuses, from homeless to presidents, make posts and contribute to the endless stream of communication. Almost any person with access to the Internet can open Instagram and become exposed to thousands of visual, textual, and audio messages that users send to the public. The fact that such exposure is unprecedented implies that the current social environment has unique social features.

According to a popular belief, one such characteristic is greater social inclusion. As networks make it possible for people to find like-minded individuals more easily, it is reasonable to suggest that communities possess a positive atmosphere where members trust each other and are satisfied with their social interactions. After all, typing another person on the phone takes far less physical effort than finding the time to meet the same person and spend quality time with them. Subsequently, the more users communicate, the stronger their connections become, and the better their overall mental health should be.

However, there is another feature of modern society that is no less important – the rising depression. World Health Organization (2022) notes that the “global prevalence of anxiety and depression increased by a massive 25%” in 2020 (para. 2). Although the pandemic is definitely responsible for such a large upsurge, the mental health crisis was evident years before the first cases of coronavirus. Santomauro et al. (2021) write that “before 2020, mental disorders were leading causes of the global health-related burden, with depressive and anxiety disorders being leading contributors to this burden” (p. 1700). As a result, not only do most people today have social exposure, but they also have greater mental health pressure.

The simultaneous prevalence of these two features suggests a correlation between social media use and depression. The Internet is a part of today’s life, the influence of which is only likely to increase (Appel et al., 2020). Therefore, removing web-based communications is not a feasible way to resolve the issue of plummeting mental health. It is essential to understand the reasons why people are overwhelmed with negative emotions when they access social networks. Once the underlying problem is uncovered, it will be possible to develop strategies aimed at alleviating and preventing the onset of depression altogether.

Statement of the Problem

The effect of social media on mental health is important to study since understanding it will allow ascertaining whether Internet-based communication is adverse itself or the root of depression lies in people’s perception of it (Brunk & De Boer, 2020). Some modern lifestyle features, such as sitting and screen exposure, are harmful, thus requiring measures that would counteract the negative effects. However, if the problem is purely psychological, the solution is changing one’s attitude. The same approach applies to the correlation between social networks and the emergence of mental health issues.

The purpose of the study is to find out how social media overuse is linked to mental illness and the most appropriate ways of managing the exposure. The first research question is: is there a correlation between social media use and the emergence of mental health issues? The second question is: why do many people feel inadequate and develop psychiatric conditions after using social platforms? The third research question is: what can users do to minimize the chance of mental illnesses following social media exposure?

Research Methods

The existing research pertaining to the topic is abundant, which requires implementing sorting criteria. The scope of the identified problem allows for numerous sources to have information relevant to research questions. Not only is the exploration of social media a popular topic in news articles, but it is also a recurring theme in academic studies (The Data Team, 2018). There are thousands of articles that explore social media and its relation to mental health. In short, the idea that people’s depression may be caused by excessive use of Facebook, Instagram, Twitter, and other popular platforms is not new.

Therefore, the primary research method in this study is secondary data analysis. Multiple studies have already been conducted involving participants from different countries and different age cohorts. If the data can show any causality between social media and mental health, it has already done so. Subsequently, there is no reason to collect more primary data since it will likely reiterate the conclusions that have already been made. Instead, it would be more effective to analyze the existing papers and find common themes that would indicate the real state of affairs. It is also important to ensure that a proper form of research is chosen.

The first criterion for sorting relevant journal articles was the inclusion of longitudinal studies. The reason why this research design is prioritized lies in the incorporation of time (Hopwood et al., 2022). Most of the studies are correlational, thus showing possible cause-and-effect relationships between various phenomena. However, the downside of such an approach is that they ascertain the fact that certain tendencies transpire simultaneously, but not their causes. For instance, a correlational analysis may show that people’s depression increased simultaneously with the influx of new users onto Reddit. However, such a conclusion can also be interpreted as vice versa. In their turn, longitudinal studies ensure accuracy due to observing certain events or people over a period of time.

The second criterion was the prioritization of communities that have already been exposed to social media for a considerable amount of time. The reasoning behind it is that people in countries where the Internet is novel will not provide results relevant to the general population. For example, Cuba is a recent adopter of Web-based technologies, as its people were first exposed to social media in 2018. It is reasonable to suggest that they will inevitably be overwhelmed with content, which will complicate ascertaining the cause of mental health issues. At the same time, most of the population has already been acquainted with such platforms, and their responses are easier to explore.

The third criterion is the limitation of the publication date to three years. The reason for this is that older research may be outdated for the current years. Even though the most popular social platforms remained the same, the way they are used has evolved substantially, potentially rendering older findings obsolete. Considering that longitudinal analyses may cover decades, such a large timeframe is not useful. For example, ten years ago, few bloggers used YouTube as the primary source of income, whereas now, it can be considered mainstream. It is important because the addition of the financial aspect may have precipitated feelings of jealousy, negatively impacting mental health, which would not be observed in a study that started in 2012.

Other criteria are less specific and are commonly used in research. All articles have to be peer-reviewed and cited, which would increase their validity. They have to be written from a sociological or medical viewpoint in order to minimize the biases of other theories. The papers should be easily accessible via popular search engines, such as Google Scholar. They should also respond to key terms, such as “social media,” “mental health,” “impact,” “longitudinal,” and others that would ensure that the search results meet the sorting criteria. Finally, the authors of the studies have to be aware of common limitations associated with existing correlational research so that the longitudinal design would compensate for its drawbacks.

Literature Review

The first publication is “Social media insights into US mental health during the COVID-19 pandemic: Longitudinal analysis of Twitter data” by Valdez, Ten Thij, Bathina, Rutter, and Bollen (2020). The authors of the study noted the plummeting mental health during the pandemic and intended to explore the agenda of publicly available social media. Specifically, Twitter was chosen as the most appropriate platform for screening the attitudes and behaviors of people. The research design was a longitudinal study which analyzed tweets published between January 22 and April 9, 2020. After compiling collecting data, the researchers used a “sentiment analysis,” which allowed them to evaluate the changes in people’s psychological well-being (Valdez et al., 2020, p. 2). The selected timeframe starts with the period before lockdowns had been widely implemented and end when the quarantine measure was in full force.

The major finding of the study was that the primary cause of the worsening public mood was the pandemic, but social media exposure might have exacerbated the pressure. Although the authors did note that “for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing,” the impact of social media is primarily negative (Valdez et al., 2020, p. 1). The closest social theory that could explain the scientific outlook of this study is symbolic interaction theory. It is evident when the authors explain why they decided to omit search words, such as “coronavirus” and “pandemic,” from the data collection process – these words became associated with inherent negativity.

The second publication is “Does time spent using social media impact mental health?: An eight year longitudinal study” by Coyne, Rogers, Zurcher, Stockdale, & Booth. (2020). The authors of this study have noted the abundance of cross-sectional research and its main limitation – the absence of time-based observations. This paper is the response to such inconsistency in the form of an eight-year-long longitudinal study that focuses on 500 adolescents. Once a year, all participants were required to complete a questionnaire, the results of which would be used as research data.

The demographic of the study participants were primarily comprised of young people aged 13 at the start of the study. As teenagers grew, so did their use of social media. All the more surprising was the major finding of the study that “increased time spent on social media was not associated with increased mental health issues across development when examined at the individual level” (Coyne et al., 2020, p. 1). The authors of the research used two theories as to the theoretical framework of the entire paper – the displacement hypothesis and the uses and gratifications theory. The first one posits that “time spent engaging with social media might displace other more important activities that might be protective for mental health” (Coyne et al., 2020, p. 3). The second one suggests that mental health issues may precipitate social media overuse. Nevertheless, both theories were proven to be inaccurate by the results of this study.

The third publication is “Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England” by Viner et al. (2019). As is the case with many researchers, the authors of this study also intended to understand the causality between mental health issues and social media. However, this study focused on cyberbullying, which is an inevitable part of the Internet for many young people. The data for the research was provided by three waves of interviews conducted with more than 10 000 students of English schools. Surveys and questionnaires served as the primary data collection methods.

The major finding of the study was that mental health was affected by exposure to cyberbullying. All participants used social media in some form, and the researchers “found no impact of social media use frequency on wellbeing” (Viner et al., 2019, p. 697). However, once cyberbullying became evident, users started to lose sleep, which precipitated depression. The authors appear to favor the displacement hypothesis as the theoretical framework. However, instead of social media displacing the time necessary for recovery, in this case, it is cyberbullying. It should also be noted that the paper advocates for measures to curb cyberbullying rather than restrict access to social media.

The fourth publication is “General and alcohol-related social media use and mental health: A large-sample longitudinal study” by Erevik et al. (2021). The authors of this study also analyzed social media use and explored its connection to mental health issues. The specific of this paper is that it focuses on the influence of certain habits that are promoted via social platforms, such as alcohol consumption. The data was collected from two Web-based surveys, which were completed by 5217 participants.

The authors of this study also failed to observe a direct connection between the use of social media and mental illnesses. Specifically, they conclude that “the association between social media and mental health may be weak (if any) and of limited practical value” (Erevik et al., 2021, p. 2000). Although observing friends post content involving alcohol did encourage users to engage in similar behavior, the subsequent harm to well-being was caused by alcohol, but not social platforms. This perspective implies the uses and gratifications theory, in which alcohol overuse pushes people towards using social media.

The fifth publication is “Longitudinal association between social media use and psychological distress among adolescents” by Thorisdottir et al. (2020). The authors of this study were also skeptical of the definitive causal relationship between social media and mental health. However, they did assume that there are different ways of using social media and pondered whether a certain manner precipitates illnesses more than another. Three surveys were conducted with one-year intervals encompassing more than 2000 participants.

As is the case with previous longitudinal studies, the authors did not note the direct impact of social media use on mental health. The most noteworthy conclusion was that it is possible to “use social media actively (chatting with friends and posting content) or passively (scrolling, looking at content from others)” (Thorisdottir et al., 2020, p. 15). The more a person spends time passively browsing, the more they are likely to show symptoms of emotional distress. At the same time, the authors also reference the finding that “limiting social media use to 30 min a day for three days resulted in decreased loneliness and reduced depressive symptoms” (Thorisdottir et al., 2020, p. 13). Both these statements are in line with the displacement hypothesis.

The sixth publication is “Social media use and adolescent well-being: A narrative review of longitudinal studies” by Course-Choi and Hammond (2021). Although not a longitudinal study, this paper is important since it analyzes other studies with this design. The review of fourteen studies has shown that “FSMU [frequency of social media use] is unlikely to constitute a meaningful measure of SM [social media] use” (Course-Choi & Hammond, 2021, p. 233). However, the type of social media can indeed be differentiated, with passive use being “linked to greater depressed mood” (Course-Choi & Hammond, 2021, p. 234). Another common theme was that active use is a way of coping with depression, which is indicative of the uses and gratifications theory.

Findings

The purpose of this study is to ascertain the connection between social media and mental health and explore possible ways of mitigating the negative impact. The first major finding is that social media is not harmful itself. All reviewed articles support these findings, as authors consistently arrive at the conclusion that the impact of social platform use is determined by the manner it is used. It is entirely possible for social media to increase mental health state, as it is evidenced by observations of loneliness performed by Thorisdottir et al. (2020). Subsequently, it is important to dissect social media use further.

The second finding was that passive use of social media increases the chances of mental illnesses. It refers to the situation when a user aimlessly scrolls through posts. Course-Choi & Hammond (2021) as well as Thorisdottir et al. (2020) report on the danger of such behavior. The less a person is focused on positive emotions, the more vulnerable they are to negativity arising from cyberbullying or succumbing to temptations promoted by alcohol-related content (Viner et al., 2019; Erevik et al., 2021). Overall, passive use of social media is the most likely precursor of mental health issues.

The third finding is that active use of social media may actually be beneficial. A positive association was observed by Thorisdottir et al. (2020), who noted that the feeling of loneliness is mitigated precisely by active use of social media. Meanwhile, Course-Choi & Hammond (2021) provide evidence that it is also a way of alleviating depression and anxiety for many people. Even if it is not achieved, it is clear that mental health issues exist independently of social media. However, it is also important to mention that active use should also be restricted, so that time spent on the Internet does not become excessive.

The fourth finding is that two theories are most effective at conceptualizing the connection between mental health and social media – the displacement hypothesis and the uses and gratifications theory. The first one is useful in describing the negative influence of Web-based communications since using social platforms does take the time that can be used for activities beneficial to personal well-being. The second one allows the researchers to explain why people attempt to tackle their emotional problems by posting on Facebook – they seek connection, which is essential in alleviating depression. Combined together, they constitute a comprehensive sociological outlook on the relationship of social media to mental health.

Recommendations

The first recommendation is to promote emotional awareness among the population. Having ascertained that criticizing solely social media use is counter-productive, it is important to teach people to differentiate the source of negative emotions. Considering that users are used to advertising, it is possible to harness its opportunities to broadcast social advertisements that encourage people to question the source of their worries. The primary target of such advertising is families, as talking to a person that inspires trust is effective at identifying emotions. This recommendation is supported by the study by Davis et al. (2019) that found that increasing emotional awareness helps people manage their anxieties. The same approach can be applied to social media use.

The second recommendation is to encourage reasonable and conscious use of social media. Most researchers agree that as long as meaningful activities, such as chatting with friends and making posts, are performed, social platforms help people improve their social life and alleviate negative emotions. The most practical way of implementing this suggestion is to promote the use of restricted apps. These programs track how much time a person spends on certain platforms and make them inaccessible once the pre-defined daily time limit has expired, thus forcing users to approach social media consciously. Restricting time is supported by the paper written by Thorisdottir et al. (2019), who advocate for active use. Any excessive indulgence becomes harmful, making the drawbacks outweigh the benefits.

The third recommendation is to minimize exposure to negativity on the Internet. The reason why mental health plummeted during COVID-related lockdowns is that people would inevitably see content related to the coronavirus. The same negativity is derived from cyberbullying – had the person not encountered it, they would not have been apprehensive because of it. Zhao & Zhou (2020) advise policymakers to restrict negative news coverage and balance it with positive information. It would also be useful to combine these measures with encouraging people to restrict passive use. After all, the longer a person scrolls the newsfeed, the more likely they are to encounter negative content. Once again, social media is the means of communication that may precipitate mental illnesses but is not guaranteed to do so.

Conclusion

Altogether, it should be evident that common viewpoints linking mental health issues to social media use are incorrect. Web-based communication is not negative or positive in itself, but the way people perceive it determines whether they will develop a mental condition or not. This paper shows that there is no correlation between social use and plummeting mental health. However, the more people are exposed to negative content, such as cyberbullying and COVID news, the more likely they are to feel anxious. Increasing emotional awareness, encouraging active use of social media, and decreasing passive use will help people protect their mental well-being while using social platforms. Ultimately, the responsibility for the safety of Web-based communications lies on users rather than social platforms.

References

Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). Journal of the Academy of Marketing Science, 48(1), 79-95. Web.

Brunk, K. H., & De Boer, C. (2020). Journal of Business Ethics, 161(2), 443-458. Web.

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Computers in Human Behavior, 104, 1-10. Web.

Course-Choi, J., & Hammond, L. (2021). Social media use and adolescent well-being: A narrative review of longitudinal studies. Cyberpsychology, Behavior, and Social Networking, 24(4), 223-236. Web.

Davis, J. P., Kendall, P. C., & Suveg, C. M. (2019). Child Psychiatry & Human Development, 50(4), 557-565. Web.

Erevik, E. K., Pallesen, S., Vedaa, Ø., Andreassen, C. S., Dhir, A., & Torsheim, T. (2021). International Journal of Mental Health and Addiction, 19(6), 1991-2002. Web.

Hopwood, C. J., Bleidorn, W., & Wright, A. G. (2022). Perspectives on Psychological Science, 17(3), 884-894. Web.

Santomauro, D. F., Herrera, A. M. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., & Ferrari, A. J. (2021). The Lancet, 398(10312), 1700-1712. Web.

The Data Team. (2018). The Economist. Web.

Thorisdottir, I. E., Sigurvinsdottir, R., Asgeirsdottir, B. B., Allegrante, J. P., & Sigfusdottir, I. D. (2019). Cyberpsychology, Behavior, and Social Networking, 22(8), 535-542. Web.

Thorisdottir, I. E., Sigurvinsdottir, R., Kristjansson, A. L., Allegrante, J. P., Lilly, C. L., & Sigfusdottir, I. D. (2020). Preventive Medicine, 141, 1-17. Web.

Valdez, D., Ten Thij, M., Bathina, K., Rutter, L. A., & Bollen, J. (2020). Social media insights into US mental health during the COVID-19 pandemic: Longitudinal analysis of Twitter data. Journal of Medical Internet Research, 22(12), 1-11. Web.

Viner, R. M., Gireesh, A., Stiglic, N., Hudson, L. D., Goddings, A. L., Ward, J. L., & Nicholls, D. E. (2019). The Lancet Child & Adolescent Health, 3(10), 685-696. Web.

World Health Organization. (2022). Web.

Zhao, N., & Zhou, G. (2020). Applied Psychology: Health and Well‐Being, 12(4), 1019-1038. Web.

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