@inproceedings{potash-etal-2017-tracking,
title = "Tracking Bias in News Sources Using Social Media: the {R}ussia-{U}kraine Maidan Crisis of 2013{--}2014",
author = "Potash, Peter and
Romanov, Alexey and
Gronas, Mikhail and
Rumshisky, Anna and
Gronas, Mikhail",
editor = "Popescu, Octavian and
Strapparava, Carlo",
booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4203",
doi = "10.18653/v1/W17-4203",
pages = "13--18",
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.",
}
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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)
ACL