Suicide Ideation Detection on Social Media During COVID-19 via Adversarial and Multi-task Learning
5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021
; 12858 LNCS:140-145, 2021.
Article
in English
| Scopus | ID: covidwho-1437168
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. © 2021, Springer Nature Switzerland AG.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021
Year:
2021
Document Type:
Article
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