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EMINDS: Understanding User Behavior Progression for Mental Health Exploration on Social Media
This study introduces EMINDS, a visual analytics system for analyzing online mental health community (OMHC) data. EMINDS helps identify behavior stages and their long-term impact on mental health, aiding early intervention strategies.
Area of Science:
- Computer Science
- Mental Health Research
- Human-Computer Interaction
Background:
- Mental health is a critical societal concern.
- Online mental health communities (OMHCs) offer rich data for behavioral analysis.
- Current sequence mining methods lack the ability to track behavior progression and long-term mental health impacts.
Purpose of the Study:
- To develop EMINDS, a visual analytics system for analyzing user behavior in OMHCs.
- To extract distinct behavior stages and assess their impact on mental health over time.
- To provide interactive visualizations for understanding behavior patterns and their evolution.
Main Methods:
- Developed a novel automatic mining pipeline to identify behavior stages.
- Integrated interactive visualizations, including a pattern-centric Sankey diagram.
- Analyzed user behaviors from Reddit to evaluate the system's effectiveness.
Main Results:
- EMINDS successfully extracts distinct behavior stages and their temporal evolution.
- The system reveals patterns of user behavior impacting long-term mental health.
- Case studies and expert interviews validated the system's effectiveness and usability.
Conclusions:
- EMINDS provides a powerful tool for understanding complex user behavior in OMHCs.
- The system facilitates the identification of behavioral patterns linked to mental health trajectories.
- EMINDS supports social scientists in developing data-driven early intervention strategies for mental health.
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