Predicting Factuality of Reporting and Bias of News Media Sources
October 02, 2018 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, Preslav Nakov
arXiv ID
1810.01765
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG,
stat.ML
Citations
262
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
3 months ago
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. These are under-studied but arguably important research problems, both in their 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 medium, (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 baselines, and confirm the importance of each feature type.
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