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Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results

Author

Listed:
  • Yun Gu

    (School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China)

  • Deyuan Chen

    (School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China)

  • Xiaoqian Liu

    (Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
    Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Suicide, as an increasingly prominent social problem, has attracted widespread social attention in the mental health field. Traditional suicide clinical assessment and risk questionnaires lack timeliness and proactivity, and high-risk groups often conceal their intentions, which is not conducive to early suicide prevention. In this study, we used machine-learning algorithms to extract text features from Sina Weibo data and built a suicide risk-prediction model to predict four dimensions of the Suicide Possibility Scale—hopelessness, suicidal ideation, negative self-evaluation, and hostility—all with model validity of 0.34 or higher. Through this method, we can detect the symptoms of suicidal ideation in a more detailed way and improve the proactiveness and accuracy of suicide risk prevention and control.

Suggested Citation

  • Yun Gu & Deyuan Chen & Xiaoqian Liu, 2022. "Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results," IJERPH, MDPI, vol. 20(1), pages 1-11, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:466-:d:1017077
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    References listed on IDEAS

    as
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    4. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    5. Shaoxiong Ji & Celina Ping Yu & Sai-fu Fung & Shirui Pan & Guodong Long, 2018. "Supervised Learning for Suicidal Ideation Detection in Online User Content," Complexity, Hindawi, vol. 2018, pages 1-10, September.
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    Cited by:

    1. Wei Pan & Xianbin Wang & Wenwei Zhou & Bowen Hang & Liwen Guo, 2023. "Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches," IJERPH, MDPI, vol. 20(3), pages 1-12, February.

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