2014/11/04 by Lei Zhang, Xiaolei Huang, Zhang, Lei +7 · 1 citation
Computer Science · Psychology · Social Sciences · #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Mental Health via Writing #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #cs.CL #cs.SI
paper · pdf · doi:10.48550/arxiv.1411.0861
arxiv created 2014/11/04 · openalex publication_date 2014/11/04 · arxiv updated 2014/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors.