Learning Users Inner Thoughts and Emotion Changes for Social Media Based Suicide Risk Detection

Suicide has become a serious problem, hurting the well-being of human society. Thanks to social media, from people's linguistic posts, suicide risk detection has achieved good performance. The aim of this article is to investigate whether more significant accuracy could be achieved. Motivated by the observation that the prior solutions strived to detect suicide risk based on users explicit outer post expressions on social media, and no attempt was made to infer users' inner true thoughts and emotion changes from their normal open posts for suicide risk detection, we propose to first learn the correlations between user's normal open posts and hidden comments, trying to understand user's inner true thoughts and emotion changes from the open posts, and then detect user's suicide risk upon the generated intermediate results. The better detection performance on the microblog dataset (3,652 at-risk microblog users and 3,652 ordinary microblog users) and forum dataset (392 at-risk forum users and 108 ordinary forum users) verifies the insight that it is more effective to learn users' inner thoughts and emotion changes for social media-based suicide risk detection.