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        <title>Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014</title>
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    <name type="personal">
        <namePart type="given">Peter</namePart>
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        <namePart type="given">Alexey</namePart>
        <namePart type="family">Romanov</namePart>
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    <name type="personal">
        <namePart type="given">Mikhail</namePart>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Rumshisky</namePart>
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            <title>Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism</title>
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            <namePart type="given">Octavian</namePart>
            <namePart type="family">Popescu</namePart>
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            <namePart type="given">Carlo</namePart>
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    <abstract>This paper addresses the task of identifying the bias in news articles published during a political or social conflict. We create a silver-standard corpus based on the actions of users in social media. Specifically, we reconceptualize bias in terms of how likely a given article is to be shared or liked by each of the opposing sides. We apply our methodology to a dataset of links collected in relation to the Russia-Ukraine Maidan crisis from 2013-2014. We show that on the task of predicting which side is likely to prefer a given article, a Naive Bayes classifier can record 90.3% accuracy looking only at domain names of the news sources. The best accuracy of 93.5% is achieved by a feed forward neural network. We also apply our methodology to gold-labeled set of articles annotated for bias, where the aforementioned Naive Bayes classifier records 82.6% accuracy and a feed-forward neural networks records 85.6% accuracy.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4203</identifier>
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%0 Conference Proceedings
%T Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014
%A Potash, Peter
%A Romanov, Alexey
%A Gronas, Mikhail
%A Rumshisky, Anna
%Y Popescu, Octavian
%Y Strapparava, Carlo
%S Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F potash-etal-2017-tracking
%X This paper addresses the task of identifying the bias in news articles published during a political or social conflict. We create a silver-standard corpus based on the actions of users in social media. Specifically, we reconceptualize bias in terms of how likely a given article is to be shared or liked by each of the opposing sides. We apply our methodology to a dataset of links collected in relation to the Russia-Ukraine Maidan crisis from 2013-2014. We show that on the task of predicting which side is likely to prefer a given article, a Naive Bayes classifier can record 90.3% accuracy looking only at domain names of the news sources. The best accuracy of 93.5% is achieved by a feed forward neural network. We also apply our methodology to gold-labeled set of articles annotated for bias, where the aforementioned Naive Bayes classifier records 82.6% accuracy and a feed-forward neural networks records 85.6% accuracy.
%R 10.18653/v1/W17-4203
%U https://aclanthology.org/W17-4203/
%U https://doi.org/10.18653/v1/W17-4203
%P 13-18

Markdown (Informal)

[Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014](https://aclanthology.org/W17-4203/) (Potash et al., 2017)

• Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014 (Potash et al., 2017)

ACL

• Peter Potash, Alexey Romanov, Mikhail Gronas, Anna Rumshisky, and Mikhail Gronas. 2017. Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 13–18, Copenhagen, Denmark. Association for Computational Linguistics.