  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.
