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.

通过微博等社交媒体识别自杀高危人群，实现积极的干预系统，本研究针对新浪微博的 1041 个用户，采用中文版 Linguistic Inquiry and Word Count（LIWC）词库和 LDA 等 NLP 方法提取语言特征，基于这两种特征通过机器学习算法训练预测模型，以估算基于语言特征的自杀概率。实验结果表明，LDA 能够找到与自杀概率相关的主题并提高预测性能，此研究为预测社交网络用户自杀概率行为增加了价值。