Topic-Aware Multi-Task Mental Health Classification with Active Learning-Based Stepwise Relabeling for Social Media Conversations

원문 초록

Mental health conditions affect millions of people worldwide, yet many cases go undetected until they reach a critical stage. Social media platforms like Reddit have become spaces where people openly share their experiences, but the support is often provided by individuals without clinical training. Existing multi-task approaches jointly model emotion and sentiment for mental health classification but frequently rely on noisy labels generated by pre-trained models and overlook topic information as a useful signal. This paper introduces two key contributions: First, an existing Core Fusion Network framework is extended by adding Topic alongside existing Mental Health Condition, Emotion, and Sentiment Classification tasks, with topic labels derived from the therapist-client interactions in the CounselChat dataset. Second, an Active Learning-based Stepwise Relabeling approach is proposed to refine a small subset of noisy emotion and topic labels using pre-trained models in two stages: targeting medium-uncertainty samples first, followed by high-uncertainty samples. The predictions above a confidence threshold (0.7) are accepted to prevent error propagation. The experiments are conducted on the SuicideWatch and Mental Health dataset of 54,412 Reddit posts collected from five mental health subreddits, evaluating domain-specific transformer encoders including MentalBERT and MentalRoBERTa. The proposed Active Learning-based Stepwise Relabeling, when applied to Core Fusion Network with MentalRoBERTa, improves Mental Health Condition by 0.09%, Emotion by 0.51%, Sentiment by 0.35%, and Topic Classification by 1.57% over the baseline Core Fusion Network. The results suggest that the proposed framework significantly reduces label noise in multi-task mental health classification, and that topic information provides additional context.