Abstract
Suicide ideation detection on social media is a challenging problem due to its implicitness. In this paper, we present an approach to detect suicide ideation on social media based on a BERT-LSTM model with Adversarial and Multi-task learning (BLAM). More specifically, BLAM combines BERT model with Bi-LSTM model to extract deeper and richer features. Furthermore, emotion classification is utilized as an auxiliary task to perform multi-task learning, which enriches the extracted features with emotion information that enhances the identification of suicide. In addition, BLAM generates adversarial noise by adversarial learning improving the generalization ability of the model. Extensive experiments conducted on our collected Suicide Ideation Detection (SID) dataset demonstrate the competitive superiority of BLAM compared with the state-of-the-art methods.
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Acknowledgements
The research described in this paper has been supported by the Hong Kong Research Grants Council through a Collaborative Research Fund (project no. C1031-18G) and Shenzhen Philosophy and Social Sciences Fund in the 13th Five-year Plan (project no. SZ2018B020), P. R. China.
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Li, J. et al. (2021). Suicide Ideation Detection on Social Media During COVID-19 via Adversarial and Multi-task Learning. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_12
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DOI: https://doi.org/10.1007/978-3-030-85896-4_12
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