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.
翻译:有关COVID-19疫苗和其他疫苗的错误信息,在社交媒体上传播,据信会引发对疫苗接种的犹豫不决。在Twitter上自动识别针对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,我们介绍疫苗Lies,大量推特收集了传播两种疫苗的错误信息:COVID-19疫苗和人类帕皮洛马病毒(HPV)疫苗。错误信息目标组织在疫苗特定分类中,这些分类揭示了错误主题和关切。错误信息分类的肿瘤承诺使人们了解,关于疫苗覆盖的两种疫苗的讨论中,哪些是错误主题和关切。组织在培训、测试和开发疫苗Lies的成套做法中,要求开发新的监督方法,以探测推特上的错误信息并确定对它的立场。此外,疫苗可以作为发展以其他疫苗为对象的错误数据集的跳板。