# Beyond Risk Prediction: Evidence Grounding and Psychosocial Factor Verification for Explainable Suicide Risk Assessment

arXiv: https://arxiv.org/abs/2610.08842 · HTML: https://mlsanity.com/paper/2610.08842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels](https://mlsanity.com/paper/2410.04501/) (2024)
- [Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media](https://mlsanity.com/paper/2507.10008/) (2025)
- [Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization](https://mlsanity.com/paper/2403.15478/) (2024)
- [Suicide Risk Assessment on Social Media with Semi-Supervised Learning](https://mlsanity.com/paper/2411.12767/) (2024)
- [Hierarchical Dual-Head Model for Suicide Risk Assessment via MentalRoBERTa](https://mlsanity.com/paper/2510.20085/) (2025)

## Abstract (arXiv)

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.