Proactive Suicide Prevention Online (PSPO) : Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

6 Scopus Citations
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Author(s)

  • Xiaoqian Liu
  • Jiumo Sun
  • Bingli Sun
  • Qing Li
  • Tingshao Zhu

Detail(s)

Original languageEnglish
Article numbere11705
Journal / PublicationJournal of Medical Internet Research
Volume21
Issue number5
Online published8 May 2019
Publication statusPublished - May 2019

Link(s)

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.

Research Area(s)

  • Chinese young people, Crisis management, Machine learning, Microblog direct message, Social network, Suicide identification

Citation Format(s)

Proactive Suicide Prevention Online (PSPO) : Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors. / Liu, Xingyun; Liu, Xiaoqian; Sun, Jiumo; Yu, Nancy Xiaonan; Sun, Bingli; Li, Qing; Zhu, Tingshao.

In: Journal of Medical Internet Research, Vol. 21, No. 5, e11705, 05.2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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