research-article

Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media

Authors:

Matloob Khushi,

Adam DunnAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 995 - 1003

https://doi.org/10.1145/3543873.3587587

Published: 30 April 2023 Publication History

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.

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Cited By

Kaminsky ZMcQuaid RHellemans KPatterson ZSaad MGabrys RKendzerska TAbizaid ARobillard R(2024)Machine Learning–Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm ValidationJournal of Medical Internet Research10.2196/4992726(e49927)Online publication date: 5-Dec-2024
https://doi.org/10.2196/49927
Naseem UThapa SZhang QHu LRashid JNasim M(2023)Incorporating historical information by disentangling hidden representations for mental health surveillance on social mediaSocial Network Analysis and Mining10.1007/s13278-023-01167-914:1Online publication date: 10-Dec-2023
https://doi.org/10.1007/s13278-023-01167-9

Index Terms

Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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cover image ACM Conferences

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

Copyright © 2023 ACM.

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Published: 30 April 2023

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Kaminsky ZMcQuaid RHellemans KPatterson ZSaad MGabrys RKendzerska TAbizaid ARobillard R(2024)Machine Learning–Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm ValidationJournal of Medical Internet Research10.2196/4992726(e49927)Online publication date: 5-Dec-2024
https://doi.org/10.2196/49927
Naseem UThapa SZhang QHu LRashid JNasim M(2023)Incorporating historical information by disentangling hidden representations for mental health surveillance on social mediaSocial Network Analysis and Mining10.1007/s13278-023-01167-914:1Online publication date: 10-Dec-2023
https://doi.org/10.1007/s13278-023-01167-9

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