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        <title>Simple Open Stance Classification for Rumour Analysis</title>
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        <namePart type="given">Ahmet</namePart>
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        <namePart type="given">Leon</namePart>
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    <name type="personal">
        <namePart type="given">Kalina</namePart>
        <namePart type="family">Bontcheva</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.</abstract>
    <identifier type="citekey">aker-etal-2017-simple</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_005</identifier>
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        <date>2017-09</date>
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            <start>31</start>
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%0 Conference Proceedings
%T Simple Open Stance Classification for Rumour Analysis
%A Aker, Ahmet
%A Derczynski, Leon
%A Bontcheva, Kalina
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F aker-etal-2017-simple
%X Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.
%R 10.26615/978-954-452-049-6_005
%U https://doi.org/10.26615/978-954-452-049-6_005
%P 31-39

Markdown (Informal)

[Simple Open Stance Classification for Rumour Analysis](https://doi.org/10.26615/978-954-452-049-6_005) (Aker et al., RANLP 2017)

• Simple Open Stance Classification for Rumour Analysis (Aker et al., RANLP 2017)

ACL

• Ahmet Aker, Leon Derczynski, and Kalina Bontcheva. 2017. Simple Open Stance Classification for Rumour Analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 31–39, Varna, Bulgaria. INCOMA Ltd..