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arXiv · 2512.03495

EMINDS: Understanding User Behavior Progression for Mental Health Exploration on Social Media

Rui Sheng·Yifang Wang·Xingbo Wang·Shun Dai·Qingyu Guo·Tai-Quan Peng·Huamin Qu·Dongyu Liu
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Abstract

Mental health is an urgent societal issue, and social scientists are increasingly turning to online mental health communities (OMHCs) to analyze user behavior data for early intervention. However, existing sequence mining techniques fall short of the urgent need to explore the behavior progression of different groups (e.g., recovery or deterioration groups) and track the potential long-term impact of behaviors on mental health status. To address this issue, we introduce EMINDS, a visual analytics system built on a novel automatic mining pipeline that extracts distinct behavior stages and assesses the potential impact of frequent stage patterns on mental health status over time. The system includes a set of interactive visualizations that summarize the meaning of each behavior stage and the evolution of different stage patterns. We feature a pattern-centric Sankey diagram to reveal contextual information about the impact of stage patterns on mental health, helping experts understand the specific changes in sequences before and after a stage pattern. We evaluated the effectiveness and usability of EMINDS through two case studies and expert interviews, which examined the potential stage patterns impacting long-term mental health by analyzing user behaviors on Reddit.

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Rui Sheng, Yifang Wang, Xingbo Wang, Shun Dai, Qingyu Guo, Tai-Quan Peng, Huamin Qu, Dongyu Liu. 2025-12-03. EMINDS: Understanding User Behavior Progression for Mental Health Exploration on Social Media. https://doi.org/10.1109/tvcg.2025.3630646

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