  Convincing people to get vaccinated against COVID-19 is a key societal
challenge in the present times. As a first step towards this goal, many prior
works have relied on social media analysis to understand the specific concerns
that people have towards these vaccines, such as potential side-effects,
ineffectiveness, political factors, and so on. Though there are datasets that
broadly classify social media posts into Anti-vax and Pro-Vax labels, there is
no dataset (to our knowledge) that labels social media posts according to the
specific anti-vaccine concerns mentioned in the posts. In this paper, we have
curated CAVES, the first large-scale dataset containing about 10k COVID-19
anti-vaccine tweets labelled into various specific anti-vaccine concerns in a
multi-label setting. This is also the first multi-label classification dataset
that provides explanations for each of the labels. Additionally, the dataset
also provides class-wise summaries of all the tweets. We also perform
preliminary experiments on the dataset and show that this is a very challenging
dataset for multi-label explainable classification and tweet summarization, as
is evident by the moderate scores achieved by some state-of-the-art models. Our
dataset and codes are available at: https://github.com/sohampoddar26/caves-data
