用认知理论分析社交媒体文本，提前预警抑郁复发风险。
ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media
- 基于抑郁认知理论构建检测框架，融合注意力、记忆等心理偏差特征。
- 模型在204人数据集上达到F1 0.86，显著区分复发与非复发用户。
- 首个临床验证的复发数据集，适合心理健康与计算社会科学交叉研究。
近50%抑郁症患者存在复发风险，二次发作后该比例升至80%。尽管社交媒介上的抑郁检测已受关注，但复发检测因缺乏标注数据且难区分复发与非复发个体而长期未被深入探索。本文提出ReDepress——首个经临床验证的社交媒体复发数据集，包含204名来自Reddit的用户，由精神健康专业人员标注。不同于以往方法，本框架借鉴抑郁认知理论，将注意偏向、解释偏向、记忆偏向和反刍等心理机制融入标注与建模过程。统计分析与机器学习实验表明，认知标记能有效区分复发与非复发群体，基于Transformer的时序模型实现F1值0.86的优异表现。研究结果验证了心理学理论在真实文本数据中的适用性，凸显认知驱动的计算方法在早期复发预警中的潜力，为可扩展、低成本的心理健康干预提供新路径。
原文摘要 · Abstract (English)
Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare.
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