Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media

Abstract

A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual&#x0027;s suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93&#x0025; accuracy; and 2) among the six categories of personal factors, <i>post, personality,</i> and <i>experience</i> are the top-3 key indicators. Under these categories, <i>posted text</i>, <i>stress level</i>, <i>stress duration</i>, <i>posted image</i>, and <i>ruminant thinking</i> contribute to one&#x0027;s suicidal ideation detection.