  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.
