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Deep learning model with multi-feature fusion and label association for suicide detection

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Abstract

Suicide can cause serious harm to individuals, families, and society, and it has become a global social problem. Personal suicide ideation is concealed, and it is difficult to be accurately identified with traditional methods such as questionnaires and clinical diagnosis. With the development of the Internet, people are increasingly inclined to express their suicidal ideation on social media, where we can identify individuals with suicidal ideation. In this paper, we construct a Chinese social media suicide detection dataset, and extract the dictionary information of the posts, the user’s post time and social information. Then, we fuse the above features with deep learning methods, combine with our proposed label association mechanism, and raise a Text Convolutional Neural Network with Multi-Feature and Label Association (TCNN-MF-LA) model. Experiments show that the proposed model performs better than previous models. We also select some users in the dataset and analyze their posts to further clarify the effectiveness of the model. This work could help to enhance the identification of highest risk population groups and to avoid potentially preventable suicides.

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Acknowledgements

The authors would like to thank the anonymous referees for their helpful suggestions. This research was supported by Supercomputing Center of Lanzhou University.

Funding

This work was supported in part by the National Key Research and Development Program of China (Grant No. 2019YFA0706200), in part by the National Natural Science Foundation of China (Grant Nos. 62076113 and 62227807), and in part by the Fundamental Research Funds for the Central Universities (lzujbky-2021-66).

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Authors and Affiliations

  1. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Zepeng Li, Wenchuan Cheng, Jiawei Zhou, Zhengyi An & Bin Hu

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  1. Zepeng Li
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  2. Wenchuan Cheng
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  3. Jiawei Zhou
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  4. Zhengyi An
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  5. Bin Hu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by WC, JZ, ZA and BH. The first draft of the manuscript was written by ZL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Bin Hu.

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Li, Z., Cheng, W., Zhou, J. et al. Deep learning model with multi-feature fusion and label association for suicide detection. Multimedia Systems 29, 2193–2203 (2023). https://doi.org/10.1007/s00530-023-01090-1

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