Prior beliefs of readers impact the way in which they project meaning onto news headlines. These beliefs can influence their perception of news reliability, as well as their reaction to news, and their likelihood of spreading the misinformation through social networks. However, most prior work focuses on fact-checking veracity of news or stylometry rather than measuring impact of misinformation. We propose Misinfo Belief Frames, a formalism for understanding how readers perceive the reliability of news and the impact of misinformation. We also introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines. Misinformation frames use commonsense reasoning to uncover implications of real and fake news headlines focused on global crises: the Covid-19 pandemic and climate change. Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines (readers' trust in news headlines was affected in 29.3% of cases). This demonstrates the potential effectiveness of using generated frames to counter misinformation.
翻译：先前的读者信仰会影响他们如何在新闻头条新闻上表达其意义的方式。 这些信仰会影响他们对新闻可靠性的看法,以及他们对新闻的反应,以及他们通过社交网络传播错误信息的可能性。 但是,大多数先前的工作都侧重于对新闻的真实性进行事实检查,或对新闻或系统测量,而不是衡量错误信息的影响。 我们提议了Misinfo Filies Frames, 这是一种了解读者如何看待新闻的可靠性和错误信息的影响的正规主义。 我们还引入了MisfinfIision Frames (MBF) 文体, 数据集有66k 的推理, 超过23.5k头条头条。 错误的信息框架使用常识推理来揭示以全球危机为重点的真实和假冒新闻头条的影响: Covid-19 大流行病和气候变化。 我们使用大规模语言模型预测错误信息框架的结果显示,机器生成的推理可以影响读者对新闻头条的信任(29.3%的案例中读者对新闻头条的信赖受到影响 ) 。 这显示了使用生成框架对抗错误的潜在效果。
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