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        <title>Classification of Moral Foundations in Microblog Political Discourse</title>
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    <name type="personal">
        <namePart type="given">Kristen</namePart>
        <namePart type="family">Johnson</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.</abstract>
    <identifier type="citekey">johnson-goldwasser-2018-classification</identifier>
    <identifier type="doi">10.18653/v1/P18-1067</identifier>
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%0 Conference Proceedings
%T Classification of Moral Foundations in Microblog Political Discourse
%A Johnson, Kristen
%A Goldwasser, Dan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F johnson-goldwasser-2018-classification
%X Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.
%R 10.18653/v1/P18-1067
%U https://aclanthology.org/P18-1067
%U https://doi.org/10.18653/v1/P18-1067
%P 720-730

Markdown (Informal)

[Classification of Moral Foundations in Microblog Political Discourse](https://aclanthology.org/P18-1067) (Johnson & Goldwasser, ACL 2018)

• Classification of Moral Foundations in Microblog Political Discourse (Johnson & Goldwasser, ACL 2018)

ACL

• Kristen Johnson and Dan Goldwasser. 2018. Classification of Moral Foundations in Microblog Political Discourse. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 720–730, Melbourne, Australia. Association for Computational Linguistics.