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        <title>A Computational Linguistic Study of Personal Recovery in Bipolar Disorder</title>
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    <name type="personal">
        <namePart type="given">Glorianna</namePart>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants inWestern countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.</abstract>
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%0 Conference Proceedings
%T A Computational Linguistic Study of Personal Recovery in Bipolar Disorder
%A Jagfeld, Glorianna
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F jagfeld-2019-computational
%X Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants inWestern countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.
%R 10.18653/v1/P19-2003
%U https://aclanthology.org/P19-2003
%U https://doi.org/10.18653/v1/P19-2003
%P 17-26

Markdown (Informal)

[A Computational Linguistic Study of Personal Recovery in Bipolar Disorder](https://aclanthology.org/P19-2003) (Jagfeld, ACL 2019)

• A Computational Linguistic Study of Personal Recovery in Bipolar Disorder (Jagfeld, ACL 2019)

ACL

• Glorianna Jagfeld. 2019. A Computational Linguistic Study of Personal Recovery in Bipolar Disorder. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 17–26, Florence, Italy. Association for Computational Linguistics.