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

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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Publication Details

Journal
IEEE Transactions on Visualization and Computer Graphics
Published
2025-11-12
DOI
https://doi.org/10.1109/tvcg.2025.3630646
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
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article

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

Ruilong Sheng, Tai‐Quan Peng, Xingbo Wang, Huamin Qu et al.
IEEE Transactions on Visualization and Computer Graphics
Mental Health via Writing
article

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

Ruilong Sheng, Tai‐Quan Peng, Xingbo Wang, Huamin Qu, Yifang Wang, Dongyu Liu, Shuying Dai, Qingyu Guo
article en

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.

IEEE Transactions on Visualization and Computer GraphicsVol. 32(2)
Florida State University (US), Hong Kong University of Science and Technology (HK), Cornell University (US), The University of Tokyo (JP), University of California, Davis (US), Michigan State University (US)
Openalex Percentile: Top 61%
Mental Health via Writing
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