Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
If people with high risk of suicide can be identi ed 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 andWord 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 prob-ability based on linguistic features.The experiment results indicate that LDA can nd 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.
suicidal ideation topic model LIWC linguistic features microblog
Lei Zhang Xiaolei Huang Tianli Liu Zhenxiang Chen Tingshao Zhu
Institute of Psychology,Chinese Academy of Sciences(CAS),Beijing 100101,China;University of Jinan,Sh China Networking Information Center,Chinese Academy of Sciences,China Institute of Population Research,Peking University,China University of Jinan,Shandong 250022,China Institute of Psychology,Chinese Academy of Sciences(CAS),Beijing 100101,China;Key Lab of Intelligent
国际会议
南昌
英文
1-11
2013-09-26（万方平台首次上网日期，不代表论文的发表时间）