Multi-Task Mental Health Detection with Large Language Models under Class Imbalance

Abstract

Social media posts provide a valuable opportunity for early mental health detection because they often capture users' thoughts, emotions, and distress signals in real time, potentially enabling timely identification of individuals at risk. However, building reliable models from such data is difficult, especially because benchmark datasets are often highly imbalanced across tasks and labels. In this paper, we study how loss design and reweighting strategies affect multi-task mental health prediction on four Reddit-based corpora covering six tasks: stress detection, binary depression detection, depression severity classification, suicidal ideation detection, binary suicide-risk detection, and ..