Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013–2014

Peter Potash, Alexey Romanov, Mikhail Gronas, Anna Rumshisky, Mikhail Gronas


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.
Anthology ID:
W17-4203
Volume:
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–18
Language:
URL:
https://www.aclweb.org/anthology/W17-4203
DOI:
10.18653/v1/W17-4203
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4203.pdf