Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention

Abstract

Despite detection of suicidal ideation on social media has made great progress in recent years, people's implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance. Enlightened by the hidden "tree holes" phenomenon on microblog, where people at suicide risk tend to disclose their inner real feelings and thoughts to the microblog space whose authors have committed suicide, we explore the use of tree holes to enhance microblog-based suicide risk detection from the following two perspectives. (1) We build suicide-oriented word embeddings based on tree hole contents to strength the sensibility of suicide-related lexicons and context based on tree hole contents. (2) A two-layered attention mechanism is deployed to grasp intermittently changing points from individual's open blog streams, revealing one's inner emotional world more or less. Our experimental results show that with suicide-oriented word embeddings and attention, microblog-based suicide risk detection can achieve over 91\% accuracy. A large-scale well-labelled suicide data set is also reported in the paper.

arXiv commentAccepted to EMNLP 2019

References in corpus (5)

• Scikit-learn: Machine Learning in Python
• Suicide ideation of individuals in online social networks
• Analogical Reasoning on Chinese Morphological and Semantic Relations
• Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
• How did the Suicide Act and Speak Differently Online? Behavioral and Linguistic Features of China's Suicide Microblog Users

Cited by in corpus (2)

• Learning Models for Suicide Prediction from Social Media Posts
• Quantifying the Suicidal Tendency on Social Media: A Survey