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

TL;DR

EMINDS presents a visual analytics system for exploring how user behaviors in online mental health communities progress over time. It introduces an automatic mining pipeline that extracts behavior stages from multivariate event sequences and identifies stage patterns, augmented by a pattern-centric Sankey visualization to provide contextual understanding. The approach is validated through two Reddit-based case studies and expert interviews, demonstrating how stage patterns relate to long-term mental-health trajectories and how practitioners can derive actionable insights. The work advances intervention design by offering interpretable, scalable tools that connect granular online behaviors to population-level mental-health outcomes.

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.

Figures (8)

• Figure 1: The concepts we have adapted from social science or proposed to improve understanding of the problem. (A) The original user posting data in OHMCs is collected. (B) Behavior variables are derived from user posts. (C) Behavior types are extracted and combined to form behavior events, which will be used to form behavior sequences. (D) Behavior stages are extracted based on changes in behavior sequence over time. (E) Original behavior sequences are represented by stage progression to identify frequent stage patterns.
• Figure 2: The pipeline of the data processing and mining components. We first process the raw data into multivariate event sequences (A-C). Then the sequences will be segmented and clustered to form behavior stages (d1-d2). To present the temporal information within a behavior stage, we align the segmented sequences within a stage to obtain statistical information (d3). Additionally, frequent stage patterns are extracted (e1) through all the stage sequences. Meanwhile, the impact of each pattern on long-term mental health is calculated (e2).
• Figure 3: Impact calculation. (A) Detect the selected pattern and divide the whole sequence into two sets. (B) Decompose all the sequences into frequent patterns. (C-D) Calculate the overall positivity or negativity in the two sets.
• Figure 4: The interface, featuring: (A) Behavior Cluster View, (B) Behavior Stage View, (C) Behavior Progression View, and (D) Pattern View.
• Figure 5: (A) Two modes of the behavior stage visualization (i.e., statistical and temporal). (B) The alternative design.