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        <title>A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media</title>
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        <namePart type="given">Ana-Maria</namePart>
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    <abstract>In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of large-scale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract part-of-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.</abstract>
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%0 Conference Proceedings
%T A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
%A Bucur, Ana-Maria
%A Podina, Ioana R.
%A Dinu, Liviu P.
%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 bucur-etal-2021-psychologically
%X In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of large-scale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract part-of-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.
%U https://aclanthology.org/2021.ranlp-1.24
%P 199-207

Markdown (Informal)

[A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media](https://aclanthology.org/2021.ranlp-1.24) (Bucur et al., RANLP 2021)

• A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media (Bucur et al., RANLP 2021)

ACL

• Ana-Maria Bucur, Ioana R. Podina, and Liviu P. Dinu. 2021. A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 199–207, Held Online. INCOMA Ltd..