  Even to a simple and short news headline, readers react in a multitude of
ways: cognitively (e.g. inferring the writer's intent), emotionally (e.g.
feeling distrust), and behaviorally (e.g. sharing the news with their friends).
Such reactions are instantaneous and yet complex, as they rely on factors that
go beyond interpreting factual content of news. We propose Misinfo Reaction
Frames (MRF), a pragmatic formalism for modeling how readers might react to a
news headline. In contrast to categorical schema, our free-text dimensions
provide a more nuanced way of understanding intent beyond being benign or
malicious. We also introduce a Misinfo Reaction Frames corpus, a crowdsourced
dataset of reactions to over 25k news headlines focusing on global crises: the
Covid-19 pandemic, climate change, and cancer. Empirical results confirm that
it is indeed possible for neural models to predict the prominent patterns of
readers' reactions to previously unseen news headlines. Additionally, our user
study shows that displaying machine-generated MRF implications alongside news
headlines to readers can increase their trust in real news while decreasing
their trust in misinformation. Our work demonstrates the feasibility and
importance of pragmatic inferences on news headlines to help enhance AI-guided
misinformation detection and mitigation.
