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Hybrid Text Representation for Explainable Suicide Risk Identification on Social Media | IEEE Journals & Magazine | IEEE Xplore

Hybrid Text Representation for Explainable Suicide Risk Identification on Social Media


Abstract:

Social media data that characterize users can provide mental health signals, including suicide risks. Existing methods for suicide risk identification on social media hav...Show More

Abstract:

Social media data that characterize users can provide mental health signals, including suicide risks. Existing methods for suicide risk identification on social media have demonstrated promising results; however, the limitation of existing methods is that they are unable to capture low- and high-level features with complex structured data on social media and are incapable of explaining the predicted labels. Explainable models are more useful when translated, so we aimed to evaluate a novel method that would produce explainable models. This article presents a hybrid text representation method that integrates word and document-level text representations to explain suicide risk identification on social media. The proposed method is then fed to a transformer-based encoder with ordinal classification to determine suicide risk. Our results show that our method outperforms state-of-the-art baselines with an FScore of 0.79 (an absolute increase of 15%) on a public suicide dataset. Our method shows that an explainable model can perform at a comparable level to the best nonexplainable models but has advantages if translated for use in clinical and public health practice.
Published in: IEEE Transactions on Computational Social Systems ( Volume: 11, Issue: 4, August 2024)
Page(s): 4663 - 4672
Date of Publication: 04 July 2022

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