Abstract:

In recent years, there has been a lot of concern about "fake news" driving polarization of society. However, recent research has shown that factually incorrect news is relatively rare and that bias and polarization in news reporting might arise from the selection and framing the news rather than making up news. We model this process as a three-stage process: first of all, the publisher selects what topics to report on the homepage. Conditional on reporting a topic, the publisher decides what factual statements to select (such as summarizing an event or quoting a politician). Finally, conditional on reporting a factual statement, the publisher has some leeway in framing the statement by adding slant. We use a simple sender/receiver model to understand selection and framing where the sender tries to influence the receiver by deciding on how to best assemble a news article. We then use our framework to document selection and framing in 14 US newspapers by using generative AI (ChatGPT) to disassemble the top homepage articles into unique factual statements. We measure the extent to which a statement is supporting the world-view of left-leaning and right-leaning partisans on an online crowdsourcing platform where human raters are incentivized to label each statement accurately. In a separate framing survey we compare how each statement is framed in different newspapers on a left to right spectrum.