Predicting Factuality of Reporting and Bias of News Media Sources
This addresses the under-studied issue of characterizing news media reliability, which is important for fact-checking systems and public information integrity.
The study tackled the problem of predicting the factuality and bias of entire news media sources, rather than individual claims or documents, and achieved sizable performance gains over baselines using features from articles, Wikipedia, Twitter, URLs, and web traffic.
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.