Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

TL;DR

This paper presents a real-time system for detecting suicidal ideation in Chinese microblogs using psychological lexicons and machine learning, aiming to enable timely intervention and prevention of suicide.

Contribution

It introduces a novel approach combining psychological lexicons with machine learning to identify suicidal posts on Weibo, achieving promising detection performance.

Findings

SVM classifier achieved 68.3% F-measure

Psychological lexicons improved detection accuracy

Identified 53 prior suicidal cases on Weibo

Abstract

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths.We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.