https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20883

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality.

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.

/pdf/natural-language-processing-reveals-vulnerable-mental-health-11fo5sqr7a.pdf

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

Background: The COVID-19 pandemic has caused widespread morbidity and mortality as well as disruption to people’s lives and livelihoods around the world; this has occurred as a result of both infection with the virus itself and the health protection measures taken to curb its spread. There are concerns that rates of suicide, suicidal behaviours and self-harm may rise during and in the aftermath of the pandemic. Given the likely rapidly expanding research evidence base on the pandemic’s impact on rates of suicide, suicidal behaviours and self-harm and emerging evidence about how best to mitigate such effects, it is important that the best available knowledge is made readily available to policymakers, public health specialists and clinicians as soon as is possible. To facilitate this, we plan to undertake a living systematic review focusing on suicide prevention in relation to COVID-19. Method: Regular automated searches will feed into a web-based screening system which will also host the data extraction form for included articles. Our eligibility criteria are wide and include aspects of incidence and prevalence of suicidal behaviour, effects of exposures and effects of interventions in relation to the COVID-19 pandemic, with minimal restrictions on the types of study design to be included. The outcomes assessed will be death by suicide; self-harm or attempted suicide (including hospital attendance and/or admission for these reasons); and suicidal thoughts/ideation. There will be no restriction on study type, except for single case reports. There will be no restriction on language of publication. The review will be updated at three-monthly intervals if a sufficient volume of new evidence justifies doing so. Conclusions: Our living review will provide a regular synthesis of the most up-to-date research evidence to guide public health and clinical policy to mitigate the impact of COVID-19 on suicide. Protocol registration: PROSPERO CRD42020183326 01/05/2020

/pdf/the-impact-of-the-covid-19-pandemic-on-self-harm-and-i0xyr3351n.pdf

Laurie Rumker

Papers

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

Reddit Mental Health Dataset