通过用户与好友的长期互动，提前发现隐性自杀倾向。
Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
- 结合用户长期发帖和社交圈话语，构建预测模型。
- 在1000人样本中，检测准确率比基线高10%。
- 适合做网络心理风险预警系统的研究者参考。
在社交媒体上，许多有自杀意念（SI）的人不会直接表达痛苦，而是通过日常发帖或与同伴互动间接显露迹象。早期识别这些隐性信号至关重要但极具挑战。本文将早期隐性自杀意念检测建模为前瞻性预测任务，提出一种计算框架，整合用户的纵向发帖历史及其社交邻近同伴的对话内容。采用复合网络中心性度量识别关键邻居，并对用户与邻居的交互进行时间对齐，将多层信号输入微调后的DeBERTa-v3模型。在包含1000名用户（500例病例与500名对照）的Reddit研究中，该方法在所有基线之上平均提升10%的检测性能。结果表明，同伴互动提供了有价值的预测信号，对设计能捕捉线上隐性风险表达的早期预警系统具有重要意义。
原文摘要 · Abstract (English)
On social media, several individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly. Instead, signs may surface indirectly through everyday posts or peer interactions. Detecting such implicit signals early is critical but remains challenging. We frame early and implicit SI as a forward-looking prediction task and develop a computational framework that models a user's information environment, consisting of both their longitudinal posting histories as well as the discourse of their socially proximal peers. We adopted a composite network centrality measure to identify top neighbors of a user, and temporally aligned the user's and neighbors' interactions -- integrating the multi-layered signals in a fine-tuned DeBERTa-v3 model. In a Reddit study of 1,000 (500 Case and 500 Control) users, our approach improves early and implicit SI detection by an average of 10% over all other baselines. These findings highlight that peer interactions offer valuable predictive signals and carry broader implications for designing early detection systems that capture indirect as well as masked expressions of risk in online environments.
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