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Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

Author

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  • Jingfang Liu

    (School of Management, Shanghai University, Shanghai 201800, China)

  • Mengshi Shi

    (School of Management, Shanghai University, Shanghai 201800, China)

  • Huihong Jiang

    (School of Management, Shanghai University, Shanghai 201800, China)

Abstract

Suicide has become a serious problem, and how to prevent suicide has become a very important research topic. Social media provides an ideal platform for monitoring suicidal ideation. This paper presents an integrated model for multidimensional information fusion. By integrating the best classification models determined by single and multiple features, different feature information is combined to better identify suicidal posts in online social media. This approach was assessed with a dataset formed from 40,222 posts annotated by Weibo. By integrating the best classification model of single features and multidimensional features, the proposed model ((BSC + RFS)-fs, WEC-fs) achieved 80.61% accuracy and a 79.20% F1-score. Other representative text information representation methods and demographic factors related to suicide may also be important predictors of suicide, which were not considered in this study. To the best of our knowledge, this is the good try that feature combination and ensemble algorithms have been fused to detect user-generated content with suicidal ideation. The findings suggest that feature combinations do not always work well, and that an appropriate combination strategy can make classification models work better. There are differences in the information contained in different functional carriers, and a targeted choice classification model may improve the detection rate of suicidal ideation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8197-:d:855762
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    References listed on IDEAS

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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.
    2. 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.

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