对比CNN与福克斯新闻，发现两者用不同方式构建事实可信度。
You're Not Gonna Believe This: A Computational Analysis of Factual Appeals and Sourcing in Partisan News
- 通过匹配相同事件报道，比较两家媒体的引用策略。
- CNN引用专家和文件更多，福克斯更依赖新闻报道和直接引语。
- 揭示左右媒体在构建事实时采用不同认知权威路径。
尽管媒体偏见广受研究，但事实性报道背后的认知策略仍缺乏计算分析。本文通过大规模比较CNN与福克斯新闻，聚焦新冠疫情期间及以哈战争期间的报道。为分离报道风格与话题选择，采用文章匹配策略，对47万+篇报道进行分析。结果表明，CNN报道中包含更多事实陈述，并更倾向于引用外部来源。两方在引源模式上存在显著差异：CNN通过引用专家与专业文件建立正式权威，而福克斯新闻则更常使用新闻报道与直接引语。该研究量化了不同党派媒体系统性地运用不同认知策略来建构现实，为媒体偏见研究提供了新维度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。