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        <title>On the State of Social Media Data for Mental Health Research</title>
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        <namePart type="given">Carlos</namePart>
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            <title>Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access</title>
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        <name type="personal">
            <namePart type="given">Nazli</namePart>
            <namePart type="family">Goharian</namePart>
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            <namePart type="given">Philip</namePart>
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            <namePart type="given">Molly</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.</abstract>
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    <identifier type="doi">10.18653/v1/2021.clpsych-1.2</identifier>
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%0 Conference Proceedings
%T On the State of Social Media Data for Mental Health Research
%A Harrigian, Keith
%A Aguirre, Carlos
%A Dredze, Mark
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F harrigian-etal-2021-state
%X Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.
%R 10.18653/v1/2021.clpsych-1.2
%U https://aclanthology.org/2021.clpsych-1.2
%U https://doi.org/10.18653/v1/2021.clpsych-1.2
%P 15-24

Markdown (Informal)

[On the State of Social Media Data for Mental Health Research](https://aclanthology.org/2021.clpsych-1.2) (Harrigian et al., CLPsych 2021)

• On the State of Social Media Data for Mental Health Research (Harrigian et al., CLPsych 2021)

ACL

• Keith Harrigian, Carlos Aguirre, and Mark Dredze. 2021. On the State of Social Media Data for Mental Health Research. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 15–24, Online. Association for Computational Linguistics.