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    <titleInfo>
        <title>Temporal Mental Health Dynamics on Social Media</title>
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    <name type="personal">
        <namePart type="given">Tom</namePart>
        <namePart type="family">Tabak</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <namePart type="given">Matthew</namePart>
        <namePart type="family">Purver</namePart>
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        <dateIssued>2020-12</dateIssued>
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            <title>Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020</title>
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        <name type="personal">
            <namePart type="given">Karin</namePart>
            <namePart type="family">Verspoor</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Conway</namePart>
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        <name type="personal">
            <namePart type="given">Berry</namePart>
            <namePart type="family">de Bruijn</namePart>
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        <name type="personal">
            <namePart type="given">Mark</namePart>
            <namePart type="family">Dredze</namePart>
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        <name type="personal">
            <namePart type="given">Rada</namePart>
            <namePart type="family">Mihalcea</namePart>
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    <abstract>We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant- supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depres- sion, supported by the literature. We propose a methodology for providing insight into tem- poral mental health dynamics to be utilised for strategic decision-making.</abstract>
    <identifier type="citekey">tabak-purver-2020-temporal</identifier>
    <identifier type="doi">10.18653/v1/2020.nlpcovid19-2.7</identifier>
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        <url>https://aclanthology.org/2020.nlpcovid19-2.7</url>
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    <part>
        <date>2020-12</date>
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%0 Conference Proceedings
%T Temporal Mental Health Dynamics on Social Media
%A Tabak, Tom
%A Purver, Matthew
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F tabak-purver-2020-temporal
%X We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant- supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depres- sion, supported by the literature. We propose a methodology for providing insight into tem- poral mental health dynamics to be utilised for strategic decision-making.
%R 10.18653/v1/2020.nlpcovid19-2.7
%U https://aclanthology.org/2020.nlpcovid19-2.7
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.7

Markdown (Informal)

[Temporal Mental Health Dynamics on Social Media](https://aclanthology.org/2020.nlpcovid19-2.7) (Tabak & Purver, NLP-COVID19 2020)

• Temporal Mental Health Dynamics on Social Media (Tabak & Purver, NLP-COVID19 2020)

ACL

• Tom Tabak and Matthew Purver. 2020. Temporal Mental Health Dynamics on Social Media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.