Leveraging Psychiatric Scale for Suicide Risk Detection on Social Media

Abstract

The objective of suicide risk detection on social media is to identify individuals who may attempt suicide and determine their suicide risk level based on their online behavior. Although data-driven learning models have been used to predict suicide risk levels, these models often lack theoretical support and explanation from psychiatric research. To address this issue, we propose the incorporation of professional psychiatric scales into research to provide theoretical support and explanations for our model. Our proposed Scale-based Neural Network (SNN) architecture aims to extract content associated with scales from the posting history of social media users to predict their suicide risk level. Additionally, our approach provides scale-based explanations for the model's predictions. Experimental results demonstrate that our proposed method outperforms several strong baseline methods and highlights the potential of combining psychiatric scales and computational techniques to improve suicide risk detection.