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Title: Proactive suicide prevention online (PSPO) : machine identification and crisis management for Chinese social media users with suicidal thoughts and behaviors
Authors: Liu, XY
Liu, XQ
Sun, JM
Yu, NX
Sun, BL
Li, Q 
Zhu, TS
Keywords: Suicide identification
Crisis management
Machine learning
Microblog direct message
Social network
Chinese young people
Issue Date: 2019
Publisher: JMIR Publications, Inc.
Source: Journal of medical Internet research, 8 May 2019, v. 21, no. 5, e11705, p. 1-13 How to cite?
Journal: Journal of medical Internet research 
Abstract: Background: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help.
Objective: The goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management.
Methods: We first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users' psycholinguistic texts in 1-month time slots prior to and postconsultation.
Results: A total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased.
Conclusions: The PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help.
ISSN: 1439-4456
EISSN: 1438-8871
DOI: 10.2196/11705
Rights: © Xingyun Liu, Xiaoqian Liu, Jiumo Sun, Nancy Xiaonan Yu, Bingli Sun, Qing Li, Tingshao Zhu. Originally published in theJournal of Medical Internet Research (, 01.05.2019. This is an open-access article distributed under the termsof the Creative Commons Attribution License (, which permits unrestricted use,distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical InternetResearch, is properly cited. The complete bibliographic information, a link to the original publication on,as well as this copyright and license information must be included.
The following publication Liu X, Liu X, Sun J, Yu NX, Sun B, Li Q, Zhu T. Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors. J Med Internet Res 2019;21(5):e11705, 13 pages is available at
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