Graph-based hierarchical attention network for suicide risk detection on social media

Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publicationThe ACM Web Conference 2023
Subtitle of host publicationCompanion of The World Wide Web Conference WWW 2023
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450394192, 9781450394161
Publication statusPublished - 2023
Externally publishedYes
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023


Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States


  • Suicide Detection
  • Graph Neural Network
  • Social Media


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