Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse

• Published in: Journal of medical Internet research 2023-07, Vol.25 (1), p.e46867-e46867
• Main Authors: Zhu, Jianfeng, Yalamanchi, Neha, Jin, Ruoming, Kenne, Deric R, Phan, NhatHai
• Format: Article
• Language: English
• Subjects: Activities of daily living; Analysis; Anxiety; Application programming interface; Bipolar disorder; Change agents; Clustering; Community; COVID-19; COVID-19 - epidemiology; Data mining; Drugs; Economic stress; Emotions; Factor analysis; Health problems; Health status; Humans; Internet; Life stress; Mental depression; Mental Health; Multimedia; Obsessive compulsive disorder; Original Paper; Pandemics; Physical symptoms; Post traumatic stress disorder; Public health; Regression analysis; Social factors; Social isolation; Social networks; Social relations; Substance abuse; Trends; User behavior; Young adults
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/46867

The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health. This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data. The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase. This study sheds light on the impact of COVID-19 on mental health and the related th