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Supervised Learning for Suicidal Ideation Detection in Online User Content

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  • Shaoxiong Ji
  • Celina Ping Yu
  • Sai-fu Fung
  • Shirui Pan
  • Guodong Long

Abstract

Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:6157249
    DOI: 10.1155/2018/6157249
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    Cited by:

    1. 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.
    2. Dennis Sing-wing Wong & Sai-fu Fung, 2020. "Development of the Cybercrime Rapid Identification Tool for Adolescents," IJERPH, MDPI, vol. 17(13), pages 1-13, June.
    3. 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.
    4. 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.
    5. Joseph, Simmi Marina & Citraro, Salvatore & Morini, Virginia & Rossetti, Giulio & Stella, Massimo, 2023. "Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    6. Theyazn H. H. Aldhyani & Saleh Nagi Alsubari & Ali Saleh Alshebami & Hasan Alkahtani & Zeyad A. T. Ahmed, 2022. "Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    7. Gisela Redondo-Sama & Teresa Morlà-Folch & Ana Burgués & Jelen Amador & Sveva Magaraggia, 2021. "Create Solidarity Networks: Dialogs in Reddit to Overcome Depression and Suicidal Ideation among Males," IJERPH, MDPI, vol. 18(22), pages 1-15, November.

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