Predicting Factuality of Reporting and Bias of News Media Sources

TL;DR

This paper develops a model to predict the factuality and bias of news media sources using diverse features from articles, Wikipedia, Twitter, URLs, and web traffic, aiding fact-checking efforts.

Contribution

It introduces a comprehensive feature-based approach for characterizing entire news media sources, expanding beyond claim verification to media-level analysis.

Findings

Significant performance improvements over baselines.

Features from Wikipedia, Twitter, URLs, and traffic are all impactful.

The approach supports better media source assessment.

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.