narrative

Literature Review

Conversations of social media and mental health have circulated platforms such as Twitter for years. To begin our research process, we sourced articles and studies from UCLA Scholar, UCLA Library, and other credible databases relating to the topics of Twitter and Mental Health. Articles ranged from how social media usage linked to brain changes, to quantifying changes of language used around mental health, Covid-19 and its impact on mental health on Twitter, and more. One such source, “The American Psychiatric Association Healthy Minds Social Media and Mental Health Poll,” presented a collection of statistics and visualizations in order to shed light on the effect of social media on the mental health of American adults (APA, 2022). Specifically, they found a conflict between perceptions of social media’s impact on personal mental health and its impact on society as a whole, concluding a mismatch and wide range of sentiment around social media. In another article, “#WhyWeTweetMH,” their research determined that there are 4 main reasons for this: a sense of community, raising awareness/combating stigma, a safe space for expression, and for coping/empowerment (Berry, 2017). In contrast, a study titled “Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years” examined not only sentiment and discourse about the topics but how often they were discussed and its impact on sentiment (Stupinski, 2022). With an increase of retweets and replies regarding mental health, positive sentiment dwindled. They further examined the positive correlation between mental health conversations and significant events such as mental health campaigns/events/weeks.  

Our literature review encompassed broad topics about social media’s interaction with mental health, which allowed us to gain insight into the “when” and the “why” of mental health discourse on social media platforms such as Twitter. Most literature agreed with our own analysis that social media has a negative affect on user mental health, although the demographic focus was different across articles. Based on the article “Influence of social media on mental health: a systematic review,” we also gained insight into the problem of addiction and how this explains the paradox of social media usage despite its negative mood affects (Sharma, 2020). All of this information is useful for our project because we were able to compare and corroborate our own findings while gaining deeper knowledge and viewing different perspectives about our topic.

CONCLUSION

Our project focused on the effects of social media use on mental health. We focused primarily on three things: how people felt about the relationship between social media and mental health, how demographics affected these conversations, and how the nature of the platform (Twitter) influenced users’ perceptions of social media and mental health. We began by scraping and cleaning original data from Twitter, and then manipulated this data using R and Python to perform different methods of analysis. We mainly used sentimental analysis and text analysis, which we visualized through various types of visualizations, including bar charts, donut charts, word clouds, and collocate graphs. 

We found that teenagers/youths and girls were one of the most discussed demographics in regards to mental health on Twitter. Sentiments were mostly neutral, but leaned towards negative, which differed from studies where discourse did not take place on social media. Through our secondary research, we found important issues that surrounded our topic such as social media addiction and depression/anxiety, which make it imperative we continue to monitor and study this area and its effects on users. Moving forward, it might be useful to research the impacts of individual social media platforms, especially as new ones emerge. Through our project, we hope to educate and inform young adults who are active on the platform by shedding light on what others might be saying on social media and possible negative effects from usage.

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