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        <title>Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter</title>
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        <namePart type="given">Kristen</namePart>
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    <name type="personal">
        <namePart type="given">Dan</namePart>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1069</identifier>
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%0 Conference Proceedings
%T Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
%A Johnson, Kristen
%A Jin, Di
%A Goldwasser, Dan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F johnson-etal-2017-leveraging
%X Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.
%R 10.18653/v1/P17-1069
%U https://aclanthology.org/P17-1069
%U https://doi.org/10.18653/v1/P17-1069
%P 741-752

Markdown (Informal)

[Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter](https://aclanthology.org/P17-1069) (Johnson et al., ACL 2017)

• Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter (Johnson et al., ACL 2017)

ACL

• Kristen Johnson, Di Jin, and Dan Goldwasser. 2017. Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 741–752, Vancouver, Canada. Association for Computational Linguistics.