Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons
This work addresses suicide prevention in China by enabling real-time detection of suicidal posts on social media, though it is incremental as it builds on existing psychological and machine learning techniques.
The paper tackled the problem of detecting suicidal ideation in Chinese microblogs by developing a system that combines machine learning with psychological lexicons, achieving an F-measure of 68.3%, Precision of 78.9%, and Recall of 60.3% using an SVM classifier.
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%.