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        <namePart type="given">Georgi</namePart>
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    <name type="personal">
        <namePart type="given">Dimitar</namePart>
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    <name type="personal">
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    <abstract>We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. This is an under-studied, but arguably important research problem, both in its own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news media, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baseline, and reveal the importance of each feature type.</abstract>
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%0 Conference Proceedings
%T Predicting Factuality of Reporting and Bias of News Media Sources
%A Baly, Ramy
%A Karadzhov, Georgi
%A Alexandrov, Dimitar
%A Glass, James
%A Nakov, Preslav
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F baly-etal-2018-predicting
%X We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. This is an under-studied, but arguably important research problem, both in its own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news media, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baseline, and reveal the importance of each feature type.
%R 10.18653/v1/D18-1389
%U https://aclanthology.org/D18-1389
%U https://doi.org/10.18653/v1/D18-1389
%P 3528-3539

Markdown (Informal)

[Predicting Factuality of Reporting and Bias of News Media Sources](https://aclanthology.org/D18-1389) (Baly et al., EMNLP 2018)

• Predicting Factuality of Reporting and Bias of News Media Sources (Baly et al., EMNLP 2018)

ACL

• Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of Reporting and Bias of News Media Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3528–3539, Brussels, Belgium. Association for Computational Linguistics.