this light. Suppose that readers find it easy to dismiss moderately inconsistent new but not grossly in consistent news. In this case, we can reinterpret the recall function as a dismissal function. When the reader dismisses news as fallacious, he presumes the paper is bad, which is isomorphic to presuming that a newspaper whose story has been forgot ten is bad. Similarly, the fact that very big stories cannot be dismissed is equivalent to the fact that categorical thinkers change their mind to big enough news and hence remember the story. This re-interpretation produces the same results as ours. Once again newspapers with mildly in consistent news bias stories so as not to be dismissed. But for big news, they do not fe ar dismissal and rep ort the news accurately. We chose the model with recall and categoriz ation for two reasons. First, it better m at ches descriptive accounts of the media, which suggest that new ers are inter ested in writing a com pelling story. The commonly used phrase "narrative im perative captures exactly the pressure new spapers feel to distill com plicated situations into a simple message. They cater to readers prior pre judices because this is one easy way to make stories memorable. Second, our specification provides a better fr amework for thinking about what kinds of spin o ccur. Newsp apers attempt to spin stories so that they better fit a particular category Nevertheless, aside from interpretation, the esults we provide below are the same with the altern ative model as well 3 Results 3.1 Two Types of bias begin with the case where the reader is Bayesi an and there is only one new sp aper Here, the newspaper has only one reason to bias the news: if it has an ideology it wants to pursue. If it has no ideology, it gains nothing from m anipulating the news. The first proposition form alizes this idea. 5 1A Specifically one woul d replace the recall function in equation 2 with a dismissal function that is non- inear. The proability of dismissal would be increasing as the news is further from priors until it gets large enough at which point the probability of dismissal would diminish ISAll proofs are in the appendixthis light. Suppose that readers nd it easy to dismiss moderately inconsistent news but not grossly inconsistent news. In this case, we can reinterpret the recall function as a dismissal function. When the reader dismisses news as fallacious, he presumes the paper is bad, which is isomorphic to presuming that a newspaper whose story has been forgotten is bad. Similarly, the fact that very big stories cannot be dismissed is equivalent to the fact that categorical thinkers change their mind to big enough news and hence remember the story.14 This re-interpretation produces the same results as ours. Once again newspapers with mildly inconsistent news bias stories so as not to be dismissed. But for big news, they do not fear dismissal and report the news accurately. We chose the model with recall and categorization for two reasons. First, it better matches descriptive accounts of the media, which suggest that newspapers are inter￾ested in writing a compelling story. The commonly used phrase \narrative imperative" captures exactly the pressure newspapers feel to distill complicated situations into a simple message. They cater to reader's prior prejudices because this is one easy way to make stories memorable. Second, our specication provides a better framework for thinking about what kinds of spin occur. Newspapers attempt to spin stories so that they better t a particular category. Nevertheless, aside from interpretation, the results we provide below are the same with the alternative model as well. 3 Results 3.1 Two Types of Bias We begin with the case where the reader is Bayesian and there is only one newspaper. Here, the newspaper has only one reason to bias the news: if it has an ideology it wants to pursue. If it has no ideology, it gains nothing from manipulating the news. The rst proposition formalizes this idea.15 14Specically one would replace the recall function in equation 2 with a dismissal function that is non￾linear. The proability of dismissal would be increasing as the news is further from priors until it gets large enough at which point the probability of dismissal would diminish. 15All proofs are in the appendix. 10