  Since the onset of the COVID-19 pandemic, vaccines have been an important
topic in public discourse. The discussions around vaccines are polarized as
some see them as an important measure to end the pandemic, and others are
hesitant or find them harmful. This study investigates posts related to
COVID-19 vaccines on Twitter and focuses on those which have a negative stance
toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19
vaccines was collected covering the period from March 1, 2020, to July 31,
2021. We used the Scikit-learn Python library to apply a support vector machine
(SVM) classifier to identify the tweets with a negative stance toward the
COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier,
out of which a subset of 2,484 tweets were manually annotated by us and made
publicly available. We used the BERTtopic model to extract and investigate the
topics discussed within the negative tweets and how they changed over time. We
show that the negativity with respect to COVID-19 vaccines has decreased over
time along with the vaccine roll-outs. We identify 37 topics of discussion and
present their respective importance over time. We show that popular topics
consist of conspiratorial discussions such as 5G towers and microchips, but
also contain legitimate concerns around vaccination safety and side effects as
well as concerns about policies. Our study shows that even unpopular opinions
or conspiracy theories can become widespread when paired with a widely popular
discussion topic such as COVID-19 vaccines. Understanding the concerns and the
discussed topics and how they change over time is essential for policymakers
and public health authorities to provide better and in-time information and
policies, to facilitate vaccination of the population in future similar crises.
