Emotion-based Modeling of Mental Disorders on Social Media

TL;DR

This paper introduces an emotion-based model for passively detecting mental disorders like depression, anxiety, and bipolar disorder on Reddit, emphasizing emotional state transitions over content to improve generalizability across topics and time.

Contribution

The study presents a novel emotion-centric approach for mental disorder detection that outperforms content-based models in generalizability, addressing domain and topic biases.

Findings

Emotion-based model performs comparably to BERT.

Model generalizes better across different topics.

Effective in detecting emotional disorders on Reddit.

Abstract

According to the World Health Organization (WHO), one in four people will be affected by mental disorders at some point in their lives. However, in many parts of the world, patients do not actively seek professional diagnosis because of stigma attached to mental illness, ignorance of mental health and its associated symptoms. In this paper, we propose a model for passively detecting mental disorders using conversations on Reddit. Specifically, we focus on a subset of mental disorders that are characterized by distinct emotional patterns (henceforth called emotional disorders): major depressive, anxiety, and bipolar disorders. Through passive (i.e., unprompted) detection, we can encourage patients to seek diagnosis and treatment for mental disorders. Our proposed model is different from other work in this area in that our model is based entirely on the emotional states, and the transition between these states of users on Reddit, whereas prior work is typically based on content-based representations (e.g., n-grams, language model embeddings, etc). We show that content-based representation is affected by domain and topic bias and thus does not generalize, while our model, on the other hand, suppresses topic-specific information and thus generalizes well across different topics and times. We conduct experiments on our model's ability to detect different emotional disorders and on the generalizability of our model. Our experiments show that while our model performs comparably to content-based models, such as BERT, it generalizes much better across time and topic.