Detection of Mental Health from Reddit via Deep Contextualized Representations

Abstract

We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

“…While the majority of past research has compared specific psychiatric conditions with healthy control groups, more recent work has begun analyzing and identifying unique differences and discriminators among psychiatric conditions (Jiang et al, 2020;Cohan et al, 2018a;. As this area progresses, we have begun to investigate whether this technology can be used beyond detection of mental illness for detecting severity of symptomatology and prediction of acute psychiatric episodes that result in hospitalization.…”

“…To identify such users, we leverage subreddits, or forums on Reddit dedicated to specific topics. Following , we collect posts from the r/SuicideWatch (SW) subreddit, and following (Cohan et al, 2018b;Jiang et al, 2020) we collect posts from subreddits related to 8 different mental health conditions: obsessive compulsive disorder (OCD), schizophrenia (SZ), borderline personality disorder (BPD), posttraumatic stress disorder (PTSD), eating disorder (ED), major depression dis-order (MDD), general anxiety disorder (GAD) and bipolar disorder. We then use regular expression matching to extract selfidentification statements from these posts to form our candidate user pool.…”

“…While previous work has shown that regular expression matching alone is able to create high precision mental health datasets Cohan et al, 2018b;Jiang et al, 2020), it is far more difficult to automatically construct a dataset with more fine-grained information. MacAvaney et al ( 2018) created a dataset of self-disclosures of depression on Reddit, which includes manually annotated temporal information about the diagnosis date.…”

Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts

“…While the majority of past research has compared specific psychiatric conditions with healthy control groups, more recent work has begun analyzing and identifying unique differences and discriminators among psychiatric conditions (Jiang et al, 2020;Cohan et al, 2018a;Coppersmith et al, 2015). As this area progresses, we have begun to investigate whether this technology can be used beyond detection of mental illness for detecting severity of symptomatology and prediction of acute psychiatric episodes that result in hospitalization.…”

“…To identify such users, we leverage subreddits, or forums on Reddit dedicated to specific topics. Following Shing et al (2018), we collect posts from the r/SuicideWatch (SW) subreddit, and following (Cohan et al, 2018b;Jiang et al, 2020) we collect posts from subreddits related to 8 different mental health conditions: obsessive compulsive disorder (OCD), schizophrenia (SZ), borderline personality disorder (BPD), posttraumatic stress disorder (PTSD), eating disorder (ED), major depression dis-order (MDD), general anxiety disorder (GAD) and bipolar disorder. We then use regular expression matching to extract selfidentification statements from these posts to form our candidate user pool.…”