title: Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model.
abstract: BACKGROUND
  Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field.
  
  
  OBJECTIVE
  The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data.
  
  
  METHODS
  There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric "Screening Efficiency" that were adopted to evaluate model effectiveness.
  
  
  RESULTS
  Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%.
  
  
  CONCLUSIONS
  Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media.
authors.affiliation: Key Lab of Behavioral Science of Chinese Academy of Sciences Institute of Psychology Chinese Academy of Sciences Beijing China ; University of Chinese Academy of Sciences Beijing China.
authors.forename: Li
authors.identifier: 0000-0002-9560-8435
authors.initials: L
authors.lastname: Guan
authors.affiliation: University of Chinese Academy of Sciences Beijing China.
authors.forename: Bibo
authors.identifier: 0000-0003-1138-8365
authors.initials: B
authors.lastname: Hao
authors.affiliation: HKJC Center for Suicide Research and Prevention The University of Hong Kong Hong Kong SAR China (Hong Kong).
authors.forename: Qijin
authors.identifier: 0000-0002-6223-5345
authors.initials: Q
authors.lastname: Cheng
authors.affiliation: HKJC Center for Suicide Research and Prevention The University of Hong Kong Hong Kong SAR China (Hong Kong).
authors.forename: Paul Sf
authors.identifier: 0000-0003-1596-4120
authors.initials: PS
authors.lastname: Yip
authors.affiliation: Key Lab of Behavioral Science of Chinese Academy of Sciences Institute of Psychology Chinese Academy of Sciences Beijing China ; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China.
authors.forename: Tingshao
authors.identifier: 0000-0003-3794-4526
authors.initials: T
authors.lastname: Zhu
keywords: Chinese; classification model; microblog; suicide probability