Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines
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DOI：
10.18653/v1/2022.acl-long.222
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Saadia Gabriel;Skyler Hallinan;Maarten Sap;Pemi Nguyen;Franziska Roesner;Eunsol Choi;Yejin Choi
中科院分区：
文献类型：
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作者：
Saadia Gabriel;Skyler Hallinan;Maarten Sap;Pemi Nguyen;Franziska Roesner;Eunsol Choi;Yejin Choi
即使是一个简单而简短的新闻标题，读者也会以多种方式做出反应：认知上（例如推断作者的意图），情感上（例如感到不信任）和行为上（例如与朋友分享新闻）。这种反应是即时的，但复杂的，因为它们依赖于超越解释新闻的事实内容的因素。我们提出了错误信息反应框架（MRF），一个务实的形式主义建模读者可能会如何反应的新闻标题。与分类模式相比，我们的自由文本维度提供了一种更细致的方式来理解意图，而不仅仅是善意或恶意。我们还引入了一个Misinfo Reaction Frames语料库，这是一个众包数据集，包含了对超过25，000条新闻标题的反应，这些新闻标题专注于全球危机：新冠肺炎大流行，气候变化和癌症。实证结果证实，神经模型确实有可能预测读者对以前看不见的新闻标题的反应的突出模式。此外，我们的用户研究表明，将机器生成的MRF含义与新闻标题一起显示给读者可以增加他们对真实的新闻的信任，同时减少他们对错误信息的信任。我们的工作证明了对新闻标题进行语用推理的可行性和重要性，以帮助增强人工智能引导的错误信息检测和缓解。
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.