IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p466-d1017077.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/466/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/1/466/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jingfang Liu & Mengshi Shi & Huihong Jiang, 2022. "Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion," IJERPH, MDPI, vol. 19(13), pages 1-13, July.
    2. Yu Zhang & Feng Yu, 2018. "Which Socio-Economic Indicators Influence Collective Morality? Big Data Analysis on Online Chinese Social Media," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 792-800, March.
    3. Michal Ptaszynski & Monika Zasko-Zielinska & Michal Marcinczuk & Gniewosz Leliwa & Marcin Fortuna & Kamil Soliwoda & Ida Dziublewska & Olimpia Hubert & Pawel Skrzek & Jan Piesiewicz & Paula Karbowska , 2021. "Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach," IJERPH, MDPI, vol. 18(22), pages 1-49, November.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Fan, Rui & Xu, Ke & Zhao, Jichang, 2018. "An agent-based model for emotion contagion and competition in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 245-259.
    3. Nour El Houda Ben Amor & Mohamed Nabil Mzoughi, 2023. "Do Millennials’ Motives for Using Snapchat Influence the Effectiveness of Snap Ads?," SAGE Open, , vol. 13(3), pages 21582440231, July.
    4. Schmidt, Christoph G. & Wuttke, David A. & Heese, H. Sebastian & Wagner, Stephan M., 2023. "Antecedents of public reactions to supply chain glitches," International Journal of Production Economics, Elsevier, vol. 259(C).
    5. Mahan, Joseph E. & Seo, Won Jae & Jordan, Jeremy S. & Funk, Daniel, 2015. "Exploring the impact of social networking sites on running involvement, running behavior, and social life satisfaction," Sport Management Review, Elsevier, vol. 18(2), pages 182-192.
    6. Molina, Arturo & Fernández, Alejandra C. & Gómez, Mar & Aranda, Evangelina, 2017. "Differences in the city branding of European capitals based on online vs. offline sources of information," Tourism Management, Elsevier, vol. 58(C), pages 28-39.
    7. Carmela Milano, 2015. "Democratization or else vulgarization of cultural capital? The role of social networks in theater’s audience behavior," Working Papers CEB 15-004, ULB -- Universite Libre de Bruxelles.
    8. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    9. Hassan Danaeefard & Ali Farazmand & Akram Dastyari, 2023. "The Iranian Coronavirus Pandemic (COVID-9) Crisismanship: Understanding the Contributions of National Culture, Media, Technology and Economic System," Public Organization Review, Springer, vol. 23(4), pages 1661-1682, December.
    10. Richey, Michelle & Ravishankar, M.N., 2019. "The role of frames and cultural toolkits in establishing new connections for social media innovation," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 325-333.
    11. Jamal El-Den & Pratap Adikhari & Pratap Adikhari, 2017. "Social media in the service of social entrepreneurship: Identifying factors for better services," Journal of Advances in Humanities and Social Sciences, Dr. Yi-Hsing Hsieh, vol. 3(2), pages 105-114.
    12. Smith, Andrew N. & Fischer, Eileen & Yongjian, Chen, 2012. "How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 102-113.
    13. Bo Yang & Chao Liu & Xusen Cheng & Xi Ma, 2022. "Understanding Users' Group Behavioral Decisions About Sharing Articles in Social Media: An Elaboration Likelihood Model Perspective," Group Decision and Negotiation, Springer, vol. 31(4), pages 819-842, August.
    14. Gal-Tzur, Ayelet & Grant-Muller, Susan M. & Kuflik, Tsvi & Minkov, Einat & Nocera, Silvio & Shoor, Itay, 2014. "The potential of social media in delivering transport policy goals," Transport Policy, Elsevier, vol. 32(C), pages 115-123.
    15. Fathey Mohammed & Nabil Hasan Al-Kumaim & Ahmed Ibrahim Alzahrani & Yousef Fazea, 2023. "The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    16. Fazal Ur Rehman & Rosman Bin Md Yusoff & Fadillah Binti Ismail & Farwida Javed, 2019. "What is Brand? Some Insights in the Historical Development," Information Management and Business Review, AMH International, vol. 10(4), pages 8-13.
    17. Drummond, Conor & O'Toole, Thomas & McGrath, Helen, 2022. "Social Media resourcing of an entrepreneurial firm network: Collaborative mobilisation processes," Journal of Business Research, Elsevier, vol. 145(C), pages 171-187.
    18. Bojana Suzić & Miroslav Karlíček & Václav Stříteský, 2016. "Adoption of Social Media for Public Relations by Museums," Central European Business Review, Prague University of Economics and Business, vol. 2016(2), pages 5-16.
    19. Monica Patrut, 2015. "Candidates In The Presidential Elections In Romania (2014): The Use Of Social Media In Political Marketing," Studies and Scientific Researches. Economics Edition, "Vasile Alecsandri" University of Bacau, Faculty of Economic Sciences, issue 21.
    20. Marcel Rosenberger & Christiane Lehrer & Reinhard Jung, 0. "Integrating data from user activities of social networks into public administrations," Information Systems Frontiers, Springer, vol. 0, pages 1-14.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:466-:d:1017077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.