Depression Detection on Social Media with Large Language Models
This work addresses the problem of early depression detection for mental healthcare by leveraging social media data, though it is incremental as it combines existing LLMs and GBT methods.
The paper tackled depression detection on social media by addressing challenges in medical knowledge integration and explainability, proposing the DORIS framework that uses LLMs for annotation and summarization, then trains a GBT classifier, achieving validated effectiveness and interpretability as a clinical tool.
Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes. Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability. To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs). To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses. These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier. Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses. Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.