You're Not Gonna Believe This: A Computational Analysis of Factual Appeals and Sourcing in Partisan News
This provides a computational analysis of epistemic strategies in partisan news, adding a new dimension to media bias research.
This paper analyzed how CNN and Fox News use different factual reporting strategies by comparing over 470K articles on the same events during the COVID-19 pandemic and Israel-Hamas war, finding CNN uses more factual statements with external sources while Fox News favors news reports and direct quotations.
While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactAppeal framework to a corpus of over 470K articles covering two highly politicized periods: the COVID-19 pandemic and the Israel-Hamas war. We find that CNN's reporting contains more factual statements and is more likely to ground them in external sources. The outlets also exhibit sharply divergent sourcing patterns: CNN builds credibility by citing Experts} and Expert Documents, constructing an appeal to formal authority, whereas Fox News favors News Reports and direct quotations. This work quantifies how partisan outlets use systematically different epistemic strategies to construct reality, adding a new dimension to the study of media bias.