发表机构
Daiichi Institute of Technology; Tokyo Gakugei University（第一工业大学; 东京学艺大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一个包含风险评估、证据锚定与因素识别的框架，利用长度路由和双验证器提升自杀风险预测的可解释性，实验显示其能提供风险等级、文本证据及细粒度心理社会因素信息。
AI 中文摘要
从社交网络服务（SNS）帖子中识别自杀风险，对于检测在线环境中的自杀相关信号具有重要意义。然而，仅进行风险分类，对预测背后的文本证据和心理社会因素提供的洞察十分有限。基于IEEE BigData 2026可解释自杀风险检测挑战赛，本研究提出了一个包含风险评估、证据锚定和因素识别三部分的框架。风险评估采用基于长度的路由机制，以适应不同长度的帖子。证据锚定识别支持性短语，并使用风险-证据约束来保持与风险预测的一致性。在因素识别方面，使用了两个验证器：分类验证器专注于因素语义，而证据感知验证器则利用特定于因素的词汇-语义线索来选择信息丰富的正向训练单元。两者的预测概率被合并以产生最终的因素预测。这三个任务使用各自特定的F1分数指标进行评估。风险评估的加权F1得分为0.8088，证据锚定的测试集宏行F1得分为0.7605，因素识别的宏F1得分为0.5562。结果表明，该框架能够提供风险预测，以及支持性的文本证据和关于心理社会因素的细粒度信息。总体而言，所提出的框架将自杀风险评估扩展到了风险水平预测之外，为SNS帖子提供了更具可解释性的分析。
英文摘要
Identifying suicide risk from social networking services (SNS) posts is important for detecting suicide-related signals in online environments. However, risk classification alone provides limited insight into the textual evidence and psychosocial factors behind a prediction. Based on the IEEE BigData 2026 Explainable Suicide Risk Detection Challenge, this study presents a framework consisting of Risk Assessment, Evidence Grounding, and Factor Identification. Risk Assessment uses length-based routing to accommodate posts of different lengths. Evidence Grounding identifies supporting phrases and uses a Risk-Evidence constraint to maintain consistency with the Risk prediction. For Factor Identification, two verifiers are used. The Taxonomy Verifier focuses on factor semantics, whereas the Evidence-Aware Verifier uses factor-specific lexical-semantic cues to select informative positive training units. Their prediction probabilities are combined to produce the final factor predictions. The three tasks are evaluated using task-specific F1 score measures. Risk Assessment achieved a Weighted F1 of 0.8088, Evidence Grounding achieved a test Macro row F1 of 0.7605, and Factor Identification achieved a Macro F1 of 0.5562. The results show that the framework can provide risk predictions, along with supporting textual evidence and fine-grained information on psychosocial factors. Overall, the proposed framework extends suicide-risk assessment beyond risk-level prediction and provides a more interpretable analysis of SNS posts.
Comments8 pages, 1 figure, 4 tables. Accepted at the 2nd Workshop on Mental Health Disorder Detection on Social Media (MHSM 2026), held in conjunction with IEEE ICDM 2026
