Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study

Abstract

Background The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world’s largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non–mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories “economic stress,” “isolation,” and “home,” while others such as “motion” significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=–0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. Conclusions By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.

Social media monitoring of mental health during disasters: A scoping review of methods and applications

“…Second, human assessment was used in 18 studies, with 11 using human annotators to conduct qualitative coding, typically for nuanced mental health information (e.g., type of social support received in Glasgow et al 2016), and 7 studies interpreting a mental health topic from a topic modeling analysis. Next, two studies used mental health forum membership to indicate mental health problems (Low et al, 2020;Saha & De Choudhury, 2017), with Saha and De Choudhury (2017) using the novel method of transfer learning from a classifier trained on a mental health subreddit (r/stress) and a random sample of Reddit posts to identify posts with high stress on college-specific subreddits following gun-related violence on campus. Finally, mental health questionnaires were used in two studies; specifically, the PHQ-9 and GAD-7 in Zhang et al (2020) and PWB Scale in Wang et al (2020).…”

“…Finally, a few articles explored specific mental health conditions rather than general negative sentiment (e.g., depression in Sadasivuni & Zhang, 2020;stress in Saha & De Choudhury, 2017;and suicide in Woo et al, 2015). One notable study by Low et al (2020) examined the impact of the initial stages of the COVID-19 pandemic on 15 mental health support groups on Reddit, allowing for disorder-specific monitoring and comparison. An increase in health anxiety and suicidality was detected across all mental health communities.…”

How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit

“…Before going into the detail, we point out that, to the best of our knowledge, this is the first work studying emotionality in social posting, within Reddit, in the COVID-19 context. Nevertheless, different studies revolving around social network analysis, either on Reddit or other platforms, have been presented [23][24][25][26][27][28].…”

“…An observational study presented in [27] employed NLP techniques in order to reveal vulnerable mental health support groups on Reddit during COVID-19. In particular, the authors characterized changes in different mental health support subreddits, such as /r/Depression and /r/SuicideWatch, finding that a particular subreddit, /r/HealthAnxiety, showed spikes in posts about COVID-19 early on in January.…”

Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment

“…The most important thing is to recognize the early signs and symptoms for depression and seek support at an early stage. Nowadays, numerous Internet forums and social media platforms enable individuals to contact each other and share their suffering, pain, and potential treatment options anonymously (Low et al, 2020 ). People worldwide can share their ideas and experiences without being exposed (Mühleck et al, 2019 ).…”