Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines
This work addresses misinformation detection and mitigation for readers and AI systems, but it appears incremental as it builds on existing frameworks with a new dataset and model.
The authors tackled the problem of modeling readers' complex reactions to news headlines by proposing Misinfo Reaction Frames (MRF), a formalism that predicts cognitive, emotional, and behavioral responses, and showed that displaying machine-generated MRF implications can increase trust in real news by unspecified amounts while decreasing trust in misinformation.
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.