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.
Original language | English |
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Title of host publication | The ACM Web Conference 2023 |
Subtitle of host publication | Companion of The World Wide Web Conference WWW 2023 |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 995-1003 |
Number of pages | 9 |
ISBN (Electronic) | 9781450394192, 9781450394161 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Event | 2023 World Wide Web Conference, WWW 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023 |
Conference
Conference | 2023 World Wide Web Conference, WWW 2023 |
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Country/Territory | United States |
City | Austin |
Period | 30/04/23 → 4/05/23 |
Keywords
- Suicide Detection
- Graph Neural Network
- Social Media