Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations
Authors: Jun Li, Xiangmeng Wang, Haoyang Li, Yifei Yan, Shijie Zhang, Hong Va Leong, Ling Feng, Nancy Xiaonan Yu, Qing Li
Organizations: The Hong Kong Polytechnic University, Hong Kong, China · City University of Hong Kong, Hong Kong, China · Shenzhen MSU-BIT University, Shenzhen, China · Tsinghua University, Beijing, China
Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g., quotes or relies) to log conversations that capture only a narrow spectrum of user interactions, and (2) They overlook hidden influences such as user conformity and suicide copycat behavior, which can significantly affect suicidal expression and propagation in online communities. To address these limitations, we propose a Multi-Agent Causal Reasoning (MACR) framework that collaboratively employs a Reasoning Agent to scale user interactions and a Bias-aware Decision-Making Agent to mitigate harmful biases arising from hidden influences. The Reasoning Agent integrates cognitive appraisal theory to generate counterfactual user reactions to posts, thereby scaling user interactions. It analyses these reactions through structured dimensions, i.e., cognitive, emotional, and behavioral patterns, with a dedicated sub-agent responsible for each dimension. The Bias-aware Decision-Making Agent mitigates hidden biases through a front-door adjustment strategy, leveraging the counterfactual user reactions produced by the Reasoning Agent. Through the collaboration of reasoning and bias-aware decision making, the proposed MACR framework not only alleviates hidden biases, but also enriches contextual information of user interactions with counterfactual knowledge. Extensive experiments on real-world conversational datasets demonstrate the effectiveness and robustness of MACR in identifying suicide risk.
Figures & tables
Figure 1: (a) Example conversation tree from "r/SuicideWatch" in Reddit.(2) The limitations of current conversation tree-based methods
Figure 2: (a) The causality of conversation tree variable X and suicide risk variable Y is confounded by unobservable variable U . (b) The psychological inference generated by Reasoning Agent as a mediator variable M between conversation tree variable X and suicide risk variable Y .
Figure 3: Overview of Multi-Agent Causal Reasoning (MACR) framework.
Model
PFA Dataset
SCT Dataset
W-Prec.
W-Rec.
W-F1
W-Prec.
W-Rec.
W-F1
Category 1: Suicide Risk Prediction Models
DAFIL
0.3487 ± 0.0180
0.3519 ± 0.0215
0.3433 ± 0.0201
0.4197 ± 0.0448
0.4151 ± 0.0319
0.4103 ± 0.0391
TSAML
0.2773 ± 0.0386
0.3998 ± 0.0279
0.3083 ± 0.0354
0.2871 ± 0.0932
0.3769 ± 0.0337
0.2704 ± 0.0704
STATENet
0.2346 ± 0.0623
0.3046 ± 0.0676
0.2250 ± 0.0461
0.2308 ± 0.0736
0.2939 ± 0.0961
0.2238 ± 0.0933
SISMO
0.3092 ± 0.0269
0.3172 ± 0.0141
0.3049 ± 0.0159
0.2722 ± 0.0829
0.3902 ± 0.0317
0.3036 ± 0.0695
Table 1: Overall performance comparison on two datasets for suicide risk prediction. Best results are in bold , second best are underlined . All values are mean ± std over 5 folds. PFA: Protective Factor-Aware Dataset; SCT: Suicidal Comment Tree Dataset.
Model Variant
Weighted-Precision
Weighted-Recall
Weighted-F1
Full Model (Ours)
0.5629 ± 0.0554
0.5189 ± 0.0268
0.5108 ± 0.0345
w/o Reasoning Agent
0.4312 ± 0.0246
0.3669 ± 0.0239
0.3438 ± 0.0196
( − 23.4%)
( − 29.3%)
( − 32.7%)
w/o Decision-Making Agent
0.5492 ± 0.0466
0.4884 ± 0.0289
0.4708 ± 0.0260
( − 2.4%)
( − 5.9%)
( − 7.8%)
w/o Multi-Agent Collaboration
0.5534 ± 0.0650
0.5067 ± 0.0322
0.4942 ± 0.0390
Table 2: Ablation study on the SCT dataset. We systematically remove or modify key components to examine their contributions.
Method
Weighted-Precision
Weighted-Recall
Weighted-F1
In-Context Learning (ICL)
0.4064
0.3457
0.3247
Chain-of-Thought (CoT)
0.4077
0.3457
0.3201
Ours (Front-door)
0.4269
0.3765
0.3768
Table 3: Comparison with different prompting techniques. All methods use the same base model and comment tree information.
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.
Tianle Hu, Chen Peng, Yi-Hsin Tsai +3
Department of Information, Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo, Japan · Research and Development of Collaborative Educational Practices Graduate School of Education Tokyo Gakugei University Tokyo, Japan
Suicide is a critical global public health challenge, causing approximately 720,000 deaths each year and calling for timely, effective prevention strategies. Existing computational studies primarily focus on post-based social media platforms such as Twitter and Weibo, leaving instant messaging environments such as Telegram underexplored. Yet group chats pose distinct challenges: messages are short, fragmented, multi-party, and often rely on implicit or culturally specific expressions, making isolated post-level analysis insufficient. We introduce SuiChat-CN, a Chinese group-chat benchmark for contextual suicide risk assessment. We collect public Telegram group-chat data, construct coherent conversational segments through signal-word extraction and bidirectional context expansion, and annotate user risk levels with an expert-validated, LLM-assisted paradigm. SuiChat-CN contains 13,312 contextual segments from 1,406 users, covering 258,228 raw chat messages. Extensive experiments with PLMs and more than 40 LLMs demonstrate that contextual information is essential for reliable risk assessment, while fine-tuning and partial-context evaluation further reveal the challenges of early detection in multi-party conversations. Due to ethical and sensitivity concerns, the dataset is not publicly released but will be shared with accredited mental health and suicide-prevention research institutions upon reasonable request.
Xiangyu Wang, Zhiwei Yu, Chengze Du +3
University of Chinese Academy of Sciences · 2Tsinghua University · 3Beijing University of Posts and Telecommunications +1
Explainable suicide-risk assessment requires models not only to estimate risk severity, but also to identify supporting language and the risk and protective factors expressed in a post. We present our system for the IEEE BigData 2026 Cup on Explainable Suicide Risk Assessment on Social Media, which addresses three tasks: risk-level classification, evidence phrase extraction, and multi-label factor identification. Our approach adapts Qwen2.5-Instruct models using quantized low-rank adaptation (QLoRA) and an answer-masked causal language-model objective. We jointly train across all three tasks for risk classification, jointly train on Tasks 1a and 1b for evidence extraction, and adapt Task 2 separately for factor identification. We also tailor aggregation to each output: we average risk-level probabilities from the 32B and 72B models, combine evidence phrases through cross-fold consensus, and calibrate factor-specific decisions through rate matching based on out-of-fold operating points. On the official leaderboard, the final system achieved a composite score of 0.7738, with 0.8089 on Task 1 and 0.6919 on Task 2. Across the evaluated configurations, three-task training performed best for Task 1a, joint training on Tasks 1a and 1b performed best for Task 1b, and task-specific training performed best for Task 2. Probability averaging further improved Task 1a when component models had complementary errors. These findings highlight the value of tailoring both training objectives and aggregation strategies to the output structure of each task within a unified language-model framework.
Xuan Zhong Feng, Geoffrey Martin, Hexin Dong +1
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA · Systems Engineering, Cornell University, New York, NY, USA