A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression

TLDR

Mental illness profoundly affects individuals, families, and society, and online communities enable sufferers to connect and provide valuable data for studying co‑occurring disorders such as anxiety and depression. The study aims to classify online communities that focus on depression within the broader context of co‑occurring mental health conditions. We crawled 620,000 posts from 80,000 users across 247 communities, extracted topic and psycholinguistic features, and applied a joint machine‑learning framework to classify depression‑focused communities. Empirical validation on the dataset shows our model outperforms recent state‑of‑the‑art baselines.

Abstract

Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620 000 posts made by 80 000 users in 247 online communities. We have extracted the topics and psycholinguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modeling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed empirical validation of the model on the crawled dataset where our model outperforms recent state-of-the-art baselines.