M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals
This addresses a critical gap in mental health detection for social media users, though it is incremental as it builds on existing disorder detection datasets.
The paper tackles the problem of identifying individuals actively seeking mental health help on social media by introducing a novel dataset, M-Help, which enables AI models to detect help-seeking behavior, diagnose conditions, and uncover root causes.
Mental health disorders are a global crisis. While various datasets exist for detecting such disorders, there remains a critical gap in identifying individuals actively seeking help. This paper introduces a novel dataset, M-Help, specifically designed to detect help-seeking behavior on social media. The dataset goes beyond traditional labels by identifying not only help-seeking activity but also specific mental health disorders and their underlying causes, such as relationship challenges or financial stressors. AI models trained on M-Help can address three key tasks: identifying help-seekers, diagnosing mental health conditions, and uncovering the root causes of issues.