arXiv cs.CL· Tianle Hu, Chen Peng, Yi-Hsin Tsai, Takshing Andy Tung, Bingyang Sun, Yenjou Wang·· 10 天前AI 评分18
超越风险预测：面向可解释自杀风险评分的证据锚定与心理社会因素验证框架
Beyond Risk Prediction: Evidence Grounding and Psychosocial Factor Verification for Explainable Suicide Risk Assessment
AI 导读
基于 IEEE BigData 2026 可解释自杀风险检测挑战赛，该研究提出由风险评估、证据锚定和因素识别三部分组成的框架。
正文 · 原文
Abstract: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.
| Comments: | 8 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 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.08842 [cs.CL] |
| (or arXiv:2610.08842v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08842 arXiv-issued DOI via DataCite |
Submission history
From: Tianle Hu [view email]
[v1]
Wed, 30 Sep 2026 12:07:01 UTC (436 KB)
来源：arXiv cs.CL · arxiv.org