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Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

  • Lei Zhang
  • Xiaolei Huang
  • Tianli Liu
  • Ang Li
  • Zhenxiang ChenEmail author
  • Tingshao ZhuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8944)

Abstract

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.

Keywords

Suicidal ideation Topic model LIWC Linguistic features Microblog 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Psychology, Chinese Academy of Sciences (CAS)BeijingChina
  2. 2.University of JinanShandongChina
  3. 3.China Networking Information Center, Chinese Academy of SciencesBeijingChina
  4. 4.Institute of Population Research, Peking UniversityBeijingChina
  5. 5.Black Dog Institute, University of New South WalesSydneyAustralia
  6. 6.Key Lab of Intelligent Information Processing, Institute of Computing Technology, CASBeijingChina

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