论文标题
战斗19号的Infodepic：建模记者，事实检查员，社交媒体平台，政策制定者和社会的观点
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
论文作者
论文摘要
随着Covid-19的大流行的出现，随着问题的提升到一个全新的水平，虚假信息的政治和医学方面融合在一起，成为首个全球不良症状。与这种疾病作斗争已被宣布为世界卫生组织最重要的重点领域之一，危险从促进假治愈方法，谣言和阴谋理论到传播异恐惧症和恐慌。解决该问题需要解决许多具有挑战性的问题，例如识别包含索赔的消息，确定其值得支票的事实和事实，以及它们造成伤害的潜力以及这种伤害的本质，仅提及一些。为了解决这一差距，我们发布了一个16K手动注释的推文的大型数据集，以进行细粒度的虚假信息分析，（i）重点介绍了Covid-19，（ii）结合了新闻工作者，事实核对人，社交媒体平台，政策制定者和社会以及（III）的观点和利益，以及（iii），涵盖阿拉伯人，Arabic，pulgarian，pulgarian，dualch，dutch和English。最后，我们使用验证的变压器显示出强大的评估结果，从而证实了单语言与多语言以及单个任务与多任务设置中数据集的实际实用性。
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.
