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        <title>Depression and Self-Harm Risk Assessment in Online Forums</title>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.</abstract>
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%0 Conference Proceedings
%T Depression and Self-Harm Risk Assessment in Online Forums
%A Yates, Andrew
%A Cohan, Arman
%A Goharian, Nazli
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yates-etal-2017-depression
%X Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.
%R 10.18653/v1/D17-1322
%U https://aclanthology.org/D17-1322/
%U https://doi.org/10.18653/v1/D17-1322
%P 2968-2978

Markdown (Informal)

[Depression and Self-Harm Risk Assessment in Online Forums](https://aclanthology.org/D17-1322/) (Yates et al., EMNLP 2017)

• Depression and Self-Harm Risk Assessment in Online Forums (Yates et al., EMNLP 2017)

ACL

• Andrew Yates, Arman Cohan, and Nazli Goharian. 2017. Depression and Self-Harm Risk Assessment in Online Forums. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2968–2978, Copenhagen, Denmark. Association for Computational Linguistics.