EMINDS：用于社交媒体上心理健康探索的用户行为演变理解
EMINDS: Understanding User Behavior Progression for Mental Health Exploration on Social Media
AI总结：
EMINDS通过自动挖掘管道和可视化分析，帮助研究用户行为演变对心理健康的影响，通过案例研究和专家访谈验证其有效性。
摘要：
心理健康是紧迫的社会问题，社会科学家越来越多地转向在线心理健康社区（OMHCs）来分析用户行为数据以进行早期干预。然而，现有的序列挖掘技术无法满足迫切需要探索不同群体（如康复或恶化群体）的行为演变以及跟踪行为对心理健康状态的潜在长期影响的需求。为了解决这一问题，我们引入了EMINDS，一个基于新颖自动挖掘管道构建的可视化分析系统，该管道提取了不同的行为阶段并评估了频繁阶段模式对心理健康状态随时间变化的潜在影响。系统包含一组交互式可视化，总结了每个行为阶段的意义以及不同阶段模式的演变。我们突出了一种以模式为中心的桑基图，以揭示阶段模式对心理健康影响的上下文信息，帮助专家理解在阶段模式前后序列中的具体变化。我们通过两个案例研究和专家访谈评估了EMINDS的有效性和可用性，这些研究通过分析Reddit上的用户行为来检验潜在阶段模式对长期心理健康的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
