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        <title>Automatic detection of stance towards vaccination in online discussion forums</title>
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        <namePart type="given">Maria</namePart>
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        <namePart type="family">Kerren</namePart>
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    <name type="personal">
        <namePart type="given">Manfred</namePart>
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            <title>Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)</title>
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        <name type="personal">
            <namePart type="given">Jitendra</namePart>
            <namePart type="family">Jonnagaddala</namePart>
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        <name type="personal">
            <namePart type="given">Hong-Jie</namePart>
            <namePart type="family">Dai</namePart>
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        <name type="personal">
            <namePart type="given">Yung-Chun</namePart>
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    <abstract>A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance ‘against’ or ‘for’ vaccination, or as ‘undecided’. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance ‘against’ vaccination from stance ‘for’ vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Future work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features.</abstract>
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%0 Conference Proceedings
%T Automatic detection of stance towards vaccination in online discussion forums
%A Skeppstedt, Maria
%A Kerren, Andreas
%A Stede, Manfred
%Y Jonnagaddala, Jitendra
%Y Dai, Hong-Jie
%Y Chang, Yung-Chun
%S Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
%D 2017
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F skeppstedt-etal-2017-automatic
%X A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance ‘against’ or ‘for’ vaccination, or as ‘undecided’. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance ‘against’ vaccination from stance ‘for’ vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Future work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features.
%U https://aclanthology.org/W17-5801
%P 1-8

Markdown (Informal)

[Automatic detection of stance towards vaccination in online discussion forums](https://aclanthology.org/W17-5801) (Skeppstedt et al., 2017)

• Automatic detection of stance towards vaccination in online discussion forums (Skeppstedt et al., 2017)

ACL

• Maria Skeppstedt, Andreas Kerren, and Manfred Stede. 2017. Automatic detection of stance towards vaccination in online discussion forums. In Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pages 1–8, Taipei, Taiwan. Association for Computational Linguistics.