  Prior beliefs of readers impact the way in which they project meaning onto
news headlines. These beliefs can influence their perception of news
reliability, as well as their reaction to news, and their likelihood of
spreading the misinformation through social networks. However, most prior work
focuses on fact-checking veracity of news or stylometry rather than measuring
impact of misinformation. We propose Misinfo Belief Frames, a formalism for
understanding how readers perceive the reliability of news and the impact of
misinformation. We also introduce the Misinfo Belief Frames (MBF) corpus, a
dataset of 66k inferences over 23.5k headlines. Misinformation frames use
commonsense reasoning to uncover implications of real and fake news headlines
focused on global crises: the Covid-19 pandemic and climate change. Our results
using large-scale language modeling to predict misinformation frames show that
machine-generated inferences can influence readers' trust in news headlines
(readers' trust in news headlines was affected in 29.3% of cases). This
demonstrates the potential effectiveness of using generated frames to counter
misinformation.
