The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020, characterising them into six story clusters before then analyse time-series and story validity trends and the level of agreement across sites. We further break down the story clusters into more granular story types by proposing a unique automated method with a BERT classifier, which can be used to classify diverse story sources, in both fact-checked stories and tweets.
翻译:2020年的冠状病毒大流行凸显了将冠状病毒错误信息挂上旗号的必要性,而事实审查小组也开始核实互联网上的错误信息。 我们探索了2020年1月至6月由实证团体PoliticFact、Poynter和Snopes报道的故事,将其分为6个故事组,然后分析时间序列和故事有效性趋势以及各站点之间的协议水平。我们进一步将故事组分解为更颗粒的故事类型,提议一种独特的自动方法,由BERT分类器来进行分类,该方法可用于对各种故事来源进行分类,包括经实证的故事和推文。