  Vaccine hesitancy continues to be a main challenge for public health
officials during the COVID-19 pandemic. As this hesitancy undermines vaccine
campaigns, many researchers have sought to identify its root causes, finding
that the increasing volume of anti-vaccine misinformation on social media
platforms is a key element of this problem. We explored Twitter as a source of
misleading content with the goal of extracting overlapping cultural and
political beliefs that motivate the spread of vaccine misinformation. To do
this, we have collected a data set of vaccine-related Tweets and annotated them
with the help of a team of annotators with a background in communications and
journalism. Ultimately we hope this can lead to effective and targeted public
health communication strategies for reaching individuals with anti-vaccine
beliefs. Moreover, this information helps with developing Machine Learning
models to automatically detect vaccine misinformation posts and combat their
negative impacts. In this paper, we present Vax-Culture, a novel Twitter
COVID-19 dataset consisting of 6373 vaccine-related tweets accompanied by an
extensive set of human-provided annotations including vaccine-hesitancy stance,
indication of any misinformation in tweets, the entities criticized and
supported in each tweet and the communicated message of each tweet. Moreover,
we define five baseline tasks including four classification and one sequence
generation tasks, and report the results of a set of recent transformer-based
models for them. The dataset and code are publicly available at
https://github.com/mrzarei5/Vax-Culture.
