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

Abstract

Suicide has become a serious problem, hurting the well-being of human society. Thanks to social media, from peopleslinguistic posts, suicide risk detection has achieved good performance. The aim of this study is to investigate whether more significantaccuracy could be achieved. Motivated by the observation that the prior solutions strived to detect suicide risk based on users explicitouter post expressions on social media, and no attempt was made to infer users inner true thoughts and emotion changes from theirnormal open posts for suicide risk detection, we propose to firstly learn the correlations between users normal open posts and hiddencomments, trying to understand users inner true thoughts and emotion changes from his open posts, and then detect users suiciderisk upon the generated intermediate results. The performance study on the microblog dataset (3,652 at-risk microblog users and 3,652ordinary microblog users) and forum dataset (392 at-risk forum users and 108 ordinary forum users) shows that, based on the inferredhidden thoughts and inner emotion changes, the proposed approach can achieve 95% and 90% detection accuracy, respectively.