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        <title>A Risk-Averse Mechanism for Suicidality Assessment on Social Media</title>
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    <abstract>Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% of incorrect predictions on real-world Reddit data. Furthermore, we discuss the qualitative, practical, and ethical aspects of SASI for suicide risk assessment as a human-in-the-loop framework.</abstract>
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%0 Conference Proceedings
%T A Risk-Averse Mechanism for Suicidality Assessment on Social Media
%A Sawhney, Ramit
%A Neerkaje, Atula
%A Gaur, Manas
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F sawhney-etal-2022-risk
%X Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% of incorrect predictions on real-world Reddit data. Furthermore, we discuss the qualitative, practical, and ethical aspects of SASI for suicide risk assessment as a human-in-the-loop framework.
%R 10.18653/v1/2022.acl-short.70
%U https://aclanthology.org/2022.acl-short.70
%U https://doi.org/10.18653/v1/2022.acl-short.70
%P 628-635

Markdown (Informal)

[A Risk-Averse Mechanism for Suicidality Assessment on Social Media](https://aclanthology.org/2022.acl-short.70) (Sawhney et al., ACL 2022)

• A Risk-Averse Mechanism for Suicidality Assessment on Social Media (Sawhney et al., ACL 2022)

ACL

• Ramit Sawhney, Atula Neerkaje, and Manas Gaur. 2022. A Risk-Averse Mechanism for Suicidality Assessment on Social Media. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 628–635, Dublin, Ireland. Association for Computational Linguistics.