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.
Framework Overview
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
Risk Assessment uses length-based routing to accommodate posts of different lengths.
Evidence Grounding
Evidence Grounding identifies supporting phrases and uses a Risk-Evidence constraint to maintain consistency with the Risk prediction.
Factor Identification
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.
Evaluation Results
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.
Conclusion
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.