Vague news and fake news
通过理论模型和新冠疫情初期的实证分析，研究了媒体报道中持续性偏见与随机噪声对公民信息处理行为的不同影响，发现噪声方差大时公民会额外调查，而可预期的偏见则无此效应。
I consider a situation in which citizens consult a report by the media to learn the state of the world. Besides the true state of the world, the report can contain two types of inaccuracies: persistent bias following from the media's ideology and random noise. Employing a theoretical model, I show that citizens perform additional investigations if there is much variance in noise. In contrast, citizens are not affected by persistent bias as they can anticipate it. These predictions are confirmed through an empirical analysis of the start of the COVID-19 outbreak, in which I utilize sentiment data to differentiate persistent bias from noise in media reports. The results suggest that media quality is more of a concern than media neutrality.