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    <titleInfo>
        <title>An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data</title>
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    <name type="personal">
        <namePart type="given">Lin</namePart>
        <namePart type="family">Miao</namePart>
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    <name type="personal">
        <namePart type="given">Mark</namePart>
        <namePart type="family">Last</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Marina</namePart>
        <namePart type="family">Litvak</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2022-05</dateIssued>
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    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text</title>
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        <name type="personal">
            <namePart type="given">Estevam</namePart>
            <namePart type="family">Hruschka</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Mitchell</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Dunja</namePart>
            <namePart type="family">Mladenic</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marko</namePart>
            <namePart type="family">Grobelnik</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Nikita</namePart>
            <namePart type="family">Bhutani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>With millions of documented recoveries from COVID-19 worldwide, various long-term sequelae have been observed in a large group of survivors. This paper is aimed at systematically analyzing user-generated conversations on Twitter that are related to long-term COVID symptoms for a better understanding of the Long COVID health consequences. Using an interactive information extraction tool built especially for this purpose, we extracted key information from the relevant tweets and analyzed the user-reported Long COVID symptoms with respect to their demographic and geographical characteristics. The results of our analysis are expected to improve the public awareness on long-term COVID-19 sequelae and provide important insights to public health authorities.</abstract>
    <identifier type="citekey">miao-etal-2022-interactive</identifier>
    <identifier type="doi">10.18653/v1/2022.wit-1.2</identifier>
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        <url>https://aclanthology.org/2022.wit-1.2</url>
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    <part>
        <date>2022-05</date>
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            <start>10</start>
            <end>19</end>
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%0 Conference Proceedings
%T An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data
%A Miao, Lin
%A Last, Mark
%A Litvak, Marina
%Y Hruschka, Estevam
%Y Mitchell, Tom
%Y Mladenic, Dunja
%Y Grobelnik, Marko
%Y Bhutani, Nikita
%S Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text
%D 2022
%8 May
%I Association for Computational Linguistics
%C (Hybrid) Dublin, Ireland, and Virtual
%F miao-etal-2022-interactive
%X With millions of documented recoveries from COVID-19 worldwide, various long-term sequelae have been observed in a large group of survivors. This paper is aimed at systematically analyzing user-generated conversations on Twitter that are related to long-term COVID symptoms for a better understanding of the Long COVID health consequences. Using an interactive information extraction tool built especially for this purpose, we extracted key information from the relevant tweets and analyzed the user-reported Long COVID symptoms with respect to their demographic and geographical characteristics. The results of our analysis are expected to improve the public awareness on long-term COVID-19 sequelae and provide important insights to public health authorities.
%R 10.18653/v1/2022.wit-1.2
%U https://aclanthology.org/2022.wit-1.2
%U https://doi.org/10.18653/v1/2022.wit-1.2
%P 10-19

Markdown (Informal)

[An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data](https://aclanthology.org/2022.wit-1.2) (Miao et al., WIT 2022)

• An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data (Miao et al., WIT 2022)

ACL

• Lin Miao, Mark Last, and Marina Litvak. 2022. An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data. In Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text, pages 10–19, (Hybrid) Dublin, Ireland, and Virtual. Association for Computational Linguistics.