Risk-Aware Suicide Detection in Social Media: A Domain-Guided Framework with Explainable LLMs

Nowadays, the close connection between people's lives and social media has led to the emergence of their psychological and emotional states in social media posts. This type of digital footprint creates a rich and novel entry point for early detection of suicide risk. Accurate detection of suicidal ideation is a significant challenge due to the high false negative rate and sensitivity to subtle linguistic features. Current AI-based suicide detection systems are unable to detect linguistic subtleties. These approaches do not consider domain-specific indicators and ignore the dynamic interaction of language, behaviour, and mental health. Identifying lexical and syntactic markers can be a powerful diagnostic lens for diagnosing psychological distress. To address these issues, we propose a new domain-based framework that integrates the specialized frequent-rare suicide vocabulary (FR-SL) into the fine-tuning process of large language models (LLMs). This vocabulary-aware strategy draws the model's attention to common and rare suicide-related phrases and enhances the model's ability to detect subtle signs of distress. In addition to improving performance on various metrics, the proposed framework adds interpretability for understanding and trusting the models' decisions while creating transparency. It also enables the design of a structure that is generalizable to the linguistic and mental health domains. The proposed approach offers clear improvements over baseline methods, especially in terms of reducing false negatives and general interpretability through transparent attribution.

Keywords:

Suicide Risk Detection , Large Language Models , Social media analysis , Mental Health Monitoring , Explainable AI