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 the factual content the news headline. Instead, understanding reactions require pragmatic understanding of the news headline, including broader background knowledge about contentious news topics as well as commonsense reasoning about people's intents and emotional reactions. We propose Misinfo Reaction Frames, a pragmatic formalism for modeling how readers might react to a news headline cognitively, emotionally, and behaviorally. We also introduce a Misinfo Reaction Frames corpus, a dataset of over 200k news headline annotated with crowdsourced reactions focusing on global crises: the Covid-19 pandemic, climate change, and cancer. Empirical results confirm that it is indeed possible to learn the prominent patterns of readers' reactions to news headlines. We also find a potentially positive use case of our model; When we present our model generated inferences to people, we find that the machine inferences can increase readers' trust in real news while decreasing their trust in misinformation. Our work demonstrates the feasibility and the importance of pragmatic inferences of news to help enhance AI-guided misinformation detection and mitigation.
翻译：即使是简单而短的新闻标题,读者也会以多种方式作出反应:认知(例如,推断作家的意图)、情感(例如,感觉不信任)和行为(例如,与朋友分享新闻),这些反应是即时的,但又复杂,因为它们依赖于超越对新闻标题的事实内容解释的因素。相反,理解反应需要务实地理解新闻标题,包括关于有争议的新闻议题的更广泛的背景知识以及关于人们意图和情绪反应的常识推理。我们建议Misinfo Reaction Frames,一种实用的形式主义,以模拟读者如何对新闻标题的认知、情感和行为反应。我们还引入了Misinfo Reaction Framcrames系统,一套超过200公里的新闻头条的数据集,以关注全球危机:Covid-19大流行病、气候变化和癌症为主的众源反应为附加注释。 令人印象深刻地证实,了解读者对新闻标题反应的显著减缓模式和常识性推理。我们还发现,在模拟中可能正面地使用对读者进行认知、情感和行为反应的案例,与此同时,我们在模拟中发现我们所显示的准确性能提升人们的信任。
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