  Although billions of COVID-19 vaccines have been administered, too many
people remain hesitant. Misinformation about the COVID-19 vaccines, propagating
on social media, is believed to drive hesitancy towards vaccination. However,
exposure to misinformation does not necessarily indicate misinformation
adoption. In this paper we describe a novel framework for identifying the
stance towards misinformation, relying on attitude consistency and its
properties. The interactions between attitude consistency, adoption or
rejection of misinformation and the content of microblogs are exploited in a
novel neural architecture, where the stance towards misinformation is organized
in a knowledge graph. This new neural framework is enabling the identification
of stance towards misinformation about COVID-19 vaccines with state-of-the-art
results. The experiments are performed on a new dataset of misinformation
towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter
discourse. Because CoVaxLies provides a taxonomy of the misinformation about
COVID-19 vaccines, we are able to show which type of misinformation is mostly
adopted and which is mostly rejected.
