Leveraging Social Media for Real-Time Interpretable and Amendable Suicide Risk Prediction With Human-in-The-Loop

Abstract

Suicide presents a global health challenge, prompting the development of diverse prevention strategies. Among them, timely identification of individuals at risk of suicide remains challenging. Although social media offers potential for tracking users’ mental status, harnessing collaboration between AI and human experts for real-time prediction of suicide risk is inadequately explored. This study presents a human-in-the-loop framework for real-time suicide risk prediction based on social media. Once a user made a new post on social media, the AI model assesses user’s suicide risk within the next month with explanation based on the historic and new posts plus domain knowledge. Human experts on the other side look into the explanation to confirm/clarify uncertain information as feedback, enabling consistent evolution of the model. Experiments on the constructed dataset, containing 66 suicidal users and 66 non-suicidal users, show that our method achieved 82.58% prediction accuracy, outperforming competitive baselines by 6.57%. Leveraging human feedback improved prediction accuracy by 4.12%. Consultation with 18 experts (including 6 medical staff and 12 psychologists) was conducted to examine the validity of our method. Ethics considerations, as well as potential and limitations of large language models in mental condition prediction, are also discussed at the end of the paper.