You're Not Gonna Believe This: A Computational Analysis of Factual Appeals and Sourcing in Partisan News
Guy Mor-Lan, Tamir Sheafer, Shaul R. Shenhav
TL;DR
This study investigates how CNN and Fox News construct factual credibility in online reporting by analyzing epistemic grounding and sourcing at scale. It introduces the FactAppeal framework to annotate how factual claims are supported, and employs a daily article-matching approach to control for topic bias across the COVID-19 and Israel-Hamas periods, analyzing 476K articles (16.5M sentences). The results reveal systematic differences: CNN favors grounding in Experts, Expert Documents, and Officials, while Fox News relies more on News Reports and Direct Quotes, with topic-dependent variations. By quantifying epistemic bias as a distinct dimension of media bias, the work provides a scalable tool for understanding how partisan outlets shape perceived authority and reality.
Abstract
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.