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Timely detection of suicidal ideation is vital for suicide prevention, as suicide continues to pose a significant global public health challenge. Social media often reflect early signs of suicidal intent, yet many predictive models lack transparency and interpretability. This study proposes an explainable AI framework combining traditional machine learning algorithms (Logistic Regression, Random Forest, SVM) and transformer-based models (BERT, DistilBERT) to detect suicidal intent in social media posts. SHAP is employed to enhance interpretability and provide insight into model decisions. Logistic Regression achieved 93% accuracy, while BERT attained 90%. The proposed framework offers transparent and actionable insights to support mental health professionals in early intervention efforts.

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Explainable Detection of Suicide Intent in Social Media Using Machine Learning and Transformer Models

  • 会议论文

  • 发表日期：

    2026年

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  • Princess Chinemerem Iloh,
  • Christian Arthur,
  • Raymond Chiong,
  • The Anh Han,
  • Alessandro Di Stefano

摘要

Timely detection of suicidal ideation is vital for suicide prevention, as suicide continues to pose a significant global public health challenge. Social media often reflect early signs of suicidal intent, yet many predictive models lack transparency and interpretability. This study proposes an explainable AI framework combining traditional machine learning algorithms (Logistic Regression, Random Forest, SVM) and transformer-based models (BERT, DistilBERT) to detect suicidal intent in social media posts. SHAP is employed to enhance interpretability and provide insight into model decisions. Logistic Regression achieved 93% accuracy, while BERT attained 90%. The proposed framework offers transparent and actionable insights to support mental health professionals in early intervention efforts.