标题： 通过社交媒体上的纵向和信息环境信号检测早期和隐性自杀意念 显示英文标题

标题： Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media

摘要： 在社交媒体上，许多有自杀意念（SI）的个体不会明确披露他们的痛苦。 相反，这些迹象可能通过日常帖子或与同伴的互动间接显现。 早期检测这种隐含信号至关重要，但仍然具有挑战性。 我们将早期且隐式的SI视为一个前瞻性预测任务，并开发了一个计算框架，该框架建模了用户的的信息环境，包括他们的长期发帖历史以及与其社会接近的同伴的讨论。 我们采用了一种复合网络中心性度量来识别用户的顶级邻居，并对用户和邻居的交互进行时间对齐——在一个微调的DeBERTa-v3模型中整合多层信号。 在一项针对1,000名（500例病例和500名对照）用户的Reddit研究中，我们的方法相比仅基于个体的基线模型，将早期和隐式SI检测提高了15%。 这些发现表明，同伴互动提供了有价值的预测信号，并对设计能够捕捉在线环境中间接和隐藏的风险表达的早期检测系统具有更广泛的意义。 显示英文摘要

摘要： On social media, many 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 15% over individual-only 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.