发表机构
University of Chicago; University of California, Berkeley（芝加哥大学; 加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过模型与机器学习衡量新闻倾向，利用记者流动数据估计其意识形态作用，发现记者解释10%的倾向差异，流动时倾向变化显著，但党派构成调整影响较小。
AI 中文摘要
记者在决定其所生产新闻的政治倾向方面扮演什么角色？我们开发并估计了一个模型，其中记者与报社在倾向和工资两方面签订契约。该模型提出了一系列条件，在这些条件下，通过利用记者在报社之间的流动，我们可以一致地估计记者偏好对报社间观察到的倾向差异的影响。为了衡量倾向，我们使用政客在推特上发布的文章训练了一个基于变换器的机器学习模型，并将其应用于2013年至2018年间美国出版的900多万篇报纸文章的全文本数据库。在我们的模型和识别假设下，我们的估计（a）拒绝了记者对其所生产内容具有零意识形态偏好的假设，（b）表明倾向差异的10%可由记者解释。当记者在报社之间流动时，他们的平均倾向变化相当于其目的地与来源报社平均值之间差距的77%。在重新加权记者党派构成的反事实中，从观察到的左倾分布转向民主党、共和党和无党派者的均匀分布，会使文章平均倾向向更保守的方向移动，但仅移动倾向标准差的0.04。
英文摘要
What role do journalists play in determining the political slant of the news they produce? We develop and estimate a model where journalists and newspaper outlets contract over both slant and wages. The model implies a set of conditions under which we can consistently estimate the role of journalist preferences in driving the observed variation in slant across outlets by leveraging journalist transitions between outlets. To measure slant, we train a transformer-based, machine learning model using articles tweeted by politicians and apply it to a full-text database of 9+ million newspaper articles published in the US between 2013 and 2018. Under our model and identifying assumptions, our estimates (a) reject the hypothesis that journalists have zero ideological preferences over the content they produce and (b) imply that 10% of the variation in slant can be explained by journalists. When journalists move across outlets, their average slant shifts by 77% of the gap between their destination and origin outlet averages. In counterfactuals that reweight journalist party composition, moving from the observed left-leaning distribution to an even distribution of Democrats, Republicans, and non-partisans shifts average article slant in a more conservative direction, but only by 0.04 standard deviations in slant.
