Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2023) Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 995-1003. From: WWW 2023: The ACM Web Conference 2023: Companion of The World Wide Web Conference, April 30 - May 4 2023, Austin, TX, USA.

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Abstract

The widespread use of social media for expressing personal thoughts and emotions makes it a valuable resource for identifying individuals at risk of suicide. Existing sequential learning-based methods have shown promising results. However, these methods may fail to capture global features. Due to its inherent ability to learn interconnected data, graph-based methods can address this gap. In this paper, we present a new graph-based hierarchical attention network (GHAN) that uses a graph convolutional neural network with an ordinal loss to improve suicide risk identification on social media. Specifically, GHAN first captures global features by constructing three graphs to capture semantic, syntactic, and sequential contextual information. Then encoded textual features are fed to attentive transformers’ encoder and optimized to factor in the increasing suicide risk levels using an ordinal classification layer hierarchically for suicide risk detection. Experimental results show that the proposed GHAN outperformed state-of-the-art methods on a public Reddit dataset.

Item ID:	79220
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4503-9416-1
Copyright Information:	© 2023 Copyright held by the owner/author(s).
Date Deposited:	08 Aug 2023 01:31
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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