  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'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% accuracy; and 2) among the six
categories of personal factors, post, personality, and experience are the top-3
key indicators. Under these categories, posted text, stress level, stress
duration, posted image, and ruminant thinking contribute to one's suicidal
ideation detection.
