SUICIDE IDEATION DETECTION WITH BIG FIVE PERSONALITY ATTENTION DEEP LEARNING MODEL

Abstract

 Detecting suicidal ideation (SI) through social media interactions has become a promising alternative for conventional questionnaire-based analysis with medical practitioners for earlier screening.  But the challenge is in how the social media texts are analyzed for occurrence of SI. Many existing works are based on word embedding and temporal trajectory of word embedding without mining the various suicide influencing factors from social media posts. As a solution to this problem, in my earlier work a deep learning model based on multiple influencing factors was proposed. Many recent works have found significant correlation of big five personality traits with SI. Motivated by this observation, in this work, the multi factor trajectory deep learning model has been integrated with big five personality trait as a moderator with risk/protective factors as influencing variables for increasing accuracy and reducing false positives in SI detection. Through integration of big five personality traits, the accuracy of the multi factor trajectory deep learning model has increased by 2%.