Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022)

Abstract: 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.

## introduction many objects, persons, and experiences in the world are framed in terms of their potential role in supporting, harming, or enhancing people's lives or interests. we can know that this is so if we know how to interpret expressions in which such things are evaluated... -charles j. @xcite epidemics and cases of disease in the 21st century are "staged" headline reader "they are lying to us." ## misinfo reaction frames corpus to construct a corpus for studying reader reactions to news headlines, we obtain 69,885 news implications (see §3.1) by eliciting annotations for 25,164 news headlines (11,757 covid related articles, 12,733 climate headlines and 674 cancer headlines). there are two stages for collecting the corpus -(1) news data collection and (2) crowdsourced annotation. ## modeling reaction frames we test the ability of large-scale language models to predict misinfo reaction frames. for free-text inferences (e.g. writer intent, reader perception), we use generative language models, specifically t5 encoder-decoder @xcite and gpt-2 decoder-only models @xcite . for categorical inferences (e.g. the gold label), we use either generative models or bert-based discriminative models @xcite . we compare neural models to a simple retrieval baseline (bert-nn) where we use gold implications aligned with the most similar headline from the training set. 12 ## experiments in this section, we evaluate the effectiveness of our proposed framework at predicting likely reactions, countering misinformation and detecting misinformation. we first describe setup for experiments ( §5.1), as well as evaluation metrics for classification and generation experiments using our corpus ( §5.2.1, §5.2.2). we also show the performance of large-scale language models on the task of generating reaction frames ( §5.3) and provide results for classification of news headlines ( §5.4). ## future directions and limitations of reaction frames our framework presents new opportunities for studying perceived intent and impact of misinfor-mation, which may also aid in countering and detecting misinformation. we can estimate content virality. given the user-annotated labels for likelihood of reading or sharing, we can estimate whether the information in the associated article is likely to propagate. we can analyze the underlying intents behind headlines. using annotated writer intents, we can determine common themes and perceived intentions in misinformation headlines across domains (e.g. mistrust of vaccination across medical domains). given the performance of predictive models highlighted by tables 5 and 6 , we can also extend this analysis to unseen headlines. we can categorize headlines by severity of likely outcomes. false headlines that explicitly incite violence, or otherwise encourage actions that lead to psychological or physical harm (e.g. not vaccinating) may be deemed more malicious than false headlines with more benign consequences (e.g. some examples of satire). future work may explore categorizing severity of headlines based on potential harms resulting from implications. perceived labels can help us understand which headlines may fool readers. we can use these labels to determine which types of misinformation headlines appear most like real news to generally knowledgeable readers. these may also help in designing misinformation countering systems and better adversarial examples to improve robustness of misinformation detection models. we can generate counter-narratives to misinformation. our results indicate it is possible to generate effective explanations for the intent of headlines that discourage trust in misinformation (section 5.3), see appendix a.5 for examples. we encourage future work that further improves performance of these models (e.g. through integration of domain knowledge). limitations. given these future directions, we also consider key limitations which must be addressed if we move beyond viewing misinfo reaction frames as a proof-of-concept and use the dataset as part of a large-scale system for evaluating or countering misinformation. since we focus on news headlines, the context is limited. the intent of a headline may be different from the actual intent of the corresponding article, especially in the case of clickbait. we find headlines to be suitable as online readers often share headlines without clicking on them @xcite , however future work may explore extending reaction frames to full news articles. there is also annotator and model bias. readers involved in our data curation and human evaluation studies are "generally knowledgeable," as proved by their ability to discern misinformation from real news. we see this bias as a potential strength as it allows us to find ways to counter misinformation in cases where readers are well-informed but still believe false information. however, annotators may have undesirable political or social biases. in such cases, gender bias may lead an annotator to assume that a politician mentioned in a headline is male or to dismiss inequality concerns raised by a scientist belonging to a minority group as "playing the race card." these biases can also appear in pre-training data, leading to model bias. 22 subjectivity in annotation is a point of discussion in many pragmatic-oriented tasks, e.g. social norm prediction @xcite and toxicity detection @xcite . we encourage conscious efforts to recruit diverse pools of annotators so multiple perspectives are considered, and future work on modeling reaction frames can consider learning algorithms that mitigate harmful effects of biases, depending on use case @xcite . lastly, we only consider english-language news and annotate with workers based in the us. it may be that news headlines would be interpreted differently in other languages and cultures. ## conclusion we introduced misinfo reaction frames, a pragmatic formalism for understanding reader perception of news reliability. we show that machinegenerated reaction frames can change perceptions of readers, and while large-scale language models are able to discern between real news and misinformation, there is still room for future work. generated reaction frames can potentially be used in a number of downstream applications, including better understanding of event causality, empathetic response generation and as counter-narratives. 22 removing these examples from data curation or trying to control for "annotator neutrality" does not erase the causes that lead to the existence of these biases. the fact that harmful biases can manifest in the viewpoints of informed readers speaks to the pervasiveness of certain stereotypes.