@inproceedings{ca67ed3875dc457cb1203f4f920fa7a0,
title = "Suicide Ideation Detection on Social Media During COVID-19 via Adversarial and Multi-task Learning",
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.",
keywords = "Adversarial learning, Multi-task learning, Suicide ideation detection",
author = "Jun Li and Zhihan Yan and Zehang Lin and Xingyun Liu and Leong, {Hong Va} and Yu, {Nancy Xiaonan} and Qing Li",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021 ; Conference date: 23-08-2021 Through 25-08-2021",
year = "2021",
doi = "10.1007/978-3-030-85896-4_12",
language = "English",
isbn = "9783030858957",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "140--145",
editor = "U, {Leong Hou} and Marc Spaniol and Yasushi Sakurai and Junying Chen",
booktitle = "Web and Big Data - 5th International Joint Conference, APWeb-WAIM 2021, Proceedings",
address = "Germany",
}