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        <title>Adapting Deep Learning Methods for Mental Health Prediction on Social Media</title>
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    <abstract>Mental health poses a significant challenge for an individual’s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users’ mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model’s word-level attention weights.</abstract>
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%0 Conference Proceedings
%T Adapting Deep Learning Methods for Mental Health Prediction on Social Media
%A Sekulic, Ivan
%A Strube, Michael
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F sekulic-strube-2019-adapting
%X Mental health poses a significant challenge for an individual’s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users’ mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model’s word-level attention weights.
%R 10.18653/v1/D19-5542
%U https://aclanthology.org/D19-5542
%U https://doi.org/10.18653/v1/D19-5542
%P 322-327

Markdown (Informal)

[Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://aclanthology.org/D19-5542) (Sekulic & Strube, WNUT 2019)

• Adapting Deep Learning Methods for Mental Health Prediction on Social Media (Sekulic & Strube, WNUT 2019)

ACL

• Ivan Sekulic and Michael Strube. 2019. Adapting Deep Learning Methods for Mental Health Prediction on Social Media. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 322–327, Hong Kong, China. Association for Computational Linguistics.