论文标题
疫苗：一种自然语言资源，用于学习识别有关Covid-19和HPV疫苗的错误信息
VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines
论文作者
论文摘要
已经施用了数十亿个Covid-19疫苗，但许多疫苗仍然犹豫。据信，在社交媒体上传播的有关Covid-19-19疫苗和其他疫苗的错误信息被认为会推动对疫苗接种的犹豫。在Twitter上自动识别靶向疫苗的错误信息的能力取决于数据资源的可用性。在本文中，我们介绍了疫苗，大量推文传播了两种疫苗的错误信息：Covid-19-19疫苗和人乳头瘤病毒（HPV）疫苗。错误的信息目标是在特定于疫苗的分类法中组织的，这些分类法揭示了错误的主题和关注。错误信息分类法的本体论承诺提供了一种理解错误信息主题和关注的理解，主导着关于疫苗覆盖的两种疫苗的讨论。该组织进行培训，测试和开发集的疫苗邀请邀请开发新型监督方法，以检测Twitter上的错误信息并确定对其的立场。此外，疫苗可能是开发针对针对其他疫苗的数据集的垫脚石。
Billions of COVID-19 vaccines have been administered, but many remain hesitant. Misinformation about the COVID-19 vaccines and other vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources. In this paper we present VaccineLies, a large collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the Human Papillomavirus (HPV) vaccines. Misinformation targets are organized in vaccine-specific taxonomies, which reveal the misinformation themes and concerns. The ontological commitments of the Misinformation taxonomies provide an understanding of which misinformation themes and concerns dominate the discourse about the two vaccines covered in VaccineLies. The organization into training, testing and development sets of VaccineLies invites the development of novel supervised methods for detecting misinformation on Twitter and identifying the stance towards it. Furthermore, VaccineLies can be a stepping stone for the development of datasets focusing on misinformation targeting additional vaccines.
