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        <title>Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020</title>
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        <namePart type="given">Arya</namePart>
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    <name type="personal">
        <namePart type="given">Giovanna</namePart>
        <namePart type="given">Maria</namePart>
        <namePart type="given">Dora</namePart>
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            <title>Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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    <abstract>We apply statistical techniques from natural language processing to a collection of Western and Hong Kong–based English-language newspaper articles spanning the years 1998–2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ between Western and Hong Kong–based sources. ANOVA on keyword frequencies reveals that Hong Kong–based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong–based papers made fewer references to police violence during the Anti–Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together, these raise questions about the existence of anodyne reporting from Hong Kong–based media. Likewise, they illustrate the importance of sample selection for protest event analysis.</abstract>
    <identifier type="citekey">scharf-etal-2021-characterizing</identifier>
    <identifier type="doi">10.18653/v1/2021.case-1.7</identifier>
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        <url>https://aclanthology.org/2021.case-1.7</url>
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        <date>2021-08</date>
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            <start>43</start>
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%0 Conference Proceedings
%T Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020
%A Scharf, James
%A McCarthy, Arya D.
%A Dore, Giovanna Maria Dora
%Y Hürriyetoğlu, Ali
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F scharf-etal-2021-characterizing
%X We apply statistical techniques from natural language processing to a collection of Western and Hong Kong–based English-language newspaper articles spanning the years 1998–2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ between Western and Hong Kong–based sources. ANOVA on keyword frequencies reveals that Hong Kong–based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong–based papers made fewer references to police violence during the Anti–Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together, these raise questions about the existence of anodyne reporting from Hong Kong–based media. Likewise, they illustrate the importance of sample selection for protest event analysis.
%R 10.18653/v1/2021.case-1.7
%U https://aclanthology.org/2021.case-1.7
%U https://doi.org/10.18653/v1/2021.case-1.7
%P 43-52

Markdown (Informal)

[Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020](https://aclanthology.org/2021.case-1.7) (Scharf et al., CASE 2021)

• Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 (Scharf et al., CASE 2021)

ACL

• James Scharf, Arya D. McCarthy, and Giovanna Maria Dora Dore. 2021. Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 43–52, Online. Association for Computational Linguistics.