Predicting Factuality of Reporting and Bias of News Media Sources

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, Preslav Nakov


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.
Anthology ID:
D18-1389
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3528–3539
Language:
URL:
https://www.aclweb.org/anthology/D18-1389
DOI:
10.18653/v1/D18-1389
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1389.pdf