Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.

翻译：对疫苗功效的极大希望最近成为对抗COVID-19大流行的斗争中的成功现实;然而,由于接触社交媒体对COVID-19疫苗的错误信息而导致疫苗犹豫不决,成为一大障碍;因此,在社交媒体上自动发现关于COVID-19疫苗的错误信息传播和讨论何种错误信息时,必须自动发现在社交媒体上传播的关于COVID-19疫苗的错误信息以及哪些类型的错误信息,因此,除采取旨在解决疫苗失灵问题的干预措施外,可在正确的时间和地点提供接种干预措施;本文件正在讨论解决对COVID-19疫苗的错误信息的第一个步骤,即自动发现在Twitter上已知的关于疫苗的错误信息,即有关CVID-19及其疫苗的社交媒体平台。我们提交CovaxLies,这是一套新的推文,这些推文被认为与若干关于COVID-19疫苗的错误目标有关,并在此基础上开发了一种新方法来检测错误信息知识。我们的方法在错误信息知识图中组织CovaxLies,因为它将错误信息识别为预测问题的一个图表链接。本文中详细列出的错误信息探测方法,在利用了在广泛连接方法上展示了目前使用的连接方法,在利用了利用了该方法进行实验性连接的方法,从而展示了当前连接的方法,从而展示了利用了该方法的高级链接的方法。