As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world’s largest social networks. Third, we provide basic user information, including the terms and characteristics of “heavy fake news” user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.

随着 SARS-CoV-2 (COVID-19) 大流行在全球范围内肆虐，错误信息的传播在全球范围内造成了混乱。因此，了解假新闻的传播和实施对策对社会福祉变得极其重要。为了支持这一事业，我们制作了一个名为 FibVID（COVID-19 的假新闻信息广播数据集）的有价值的数据集，它从三个关键角度处理 COVID-19 和非 COVID 新闻。首先，我们提供新闻项目的真假 (T/F) 指标，由多个事实核查平台（例如 Snopes 和 Politifact）标记和验证。其次，我们从 Twitter（世界上最大的社交网络之一）收集与虚假声明相关的推文和转发。三、我们提供用户基本信息，包括“重度假新闻”用户的术语和特征，以更好地理解考虑到 COVID-19 的 T/F 索赔。FibVID 提供了几个重要的贡献。它有助于揭示与识别其真实性相关的新闻项目和主题的传播模式。它还有助于对参与假新闻传播的用户进行分类和识别特征。我们还为 FibVID 的未来应用提供建议，并通过一些探索性分析来检验所用方法的有效性。它还有助于对参与假新闻传播的用户进行分类和识别特征。我们还为 FibVID 的未来应用提供建议，并通过一些探索性分析来检验所用方法的有效性。它还有助于对参与假新闻传播的用户进行分类和识别特征。我们还为 FibVID 的未来应用提供建议，并通过一些探索性分析来检验所用方法的有效性。