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        <title>Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential</title>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
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    <name type="personal">
        <namePart type="given">Olga</namePart>
        <namePart type="family">Kovaleva</namePart>
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            <title>Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
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            <namePart type="given">Alberto</namePart>
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            <namePart type="given">Chris</namePart>
            <namePart type="family">Brew</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Chris</namePart>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
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    <abstract>Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship. We investigate the possibility of their automatic detection and their potential for predicting real-world protest events, on historical data of Bolotnaya protests in Russia (2011-2013). We find that political calls to action can be annotated and detected with relatively high accuracy, and that in our sample their volume has a moderate positive correlation with rally attendance.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5005</identifier>
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%0 Conference Proceedings
%T Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential
%A Rogers, Anna
%A Kovaleva, Olga
%A Rumshisky, Anna
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F rogers-etal-2019-calls
%X Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship. We investigate the possibility of their automatic detection and their potential for predicting real-world protest events, on historical data of Bolotnaya protests in Russia (2011-2013). We find that political calls to action can be annotated and detected with relatively high accuracy, and that in our sample their volume has a moderate positive correlation with rally attendance.
%R 10.18653/v1/D19-5005
%U https://aclanthology.org/D19-5005
%U https://doi.org/10.18653/v1/D19-5005
%P 36-44

Markdown (Informal)

[Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential](https://aclanthology.org/D19-5005) (Rogers et al., NLP4IF 2019)

• Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential (Rogers et al., NLP4IF 2019)

ACL

• Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2019. Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 36–44, Hong Kong, China. Association for Computational Linguistics.