Citation

Cao L, Zhang H, Feng L. IEEE Trans. Multimedia 2022; 24: 87-102.

Copyright

(Copyright © 2022, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TMM.2020.3046867

PMID

unavailable

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'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. © 2021 IEEE.

Language: en

Keywords

social interaction; Suicidal ideation; Social networking (online); social media; Deep neural networks; Attention mechanisms; Detection performance; Knowledge graphs; Knowledge representation; Performance study; Personal interaction; Personal knowledge graph; Popular platform; Stress duration; suicidal ideation detection