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    <titleInfo>
        <title>Automatic Detection and Classification of Mental Illnesses from General Social Media Texts</title>
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        <namePart type="given">Anca</namePart>
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        <namePart type="given">Andreea-Codrina</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. (2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts.</abstract>
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%0 Conference Proceedings
%T Automatic Detection and Classification of Mental Illnesses from General Social Media Texts
%A Dinu, Anca
%A Moldovan, Andreea-Codrina
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F dinu-moldovan-2021-automatic
%X Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. (2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts.
%U https://aclanthology.org/2021.ranlp-1.41/
%P 358-366

Markdown (Informal)

[Automatic Detection and Classification of Mental Illnesses from General Social Media Texts](https://aclanthology.org/2021.ranlp-1.41/) (Dinu & Moldovan, RANLP 2021)

• Automatic Detection and Classification of Mental Illnesses from General Social Media Texts (Dinu & Moldovan, RANLP 2021)

ACL

• Anca Dinu and Andreea-Codrina Moldovan. 2021. Automatic Detection and Classification of Mental Illnesses from General Social Media Texts. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 358–366, Held Online. INCOMA Ltd..