  Although multiple COVID-19 vaccines have been available for several months
now, vaccine hesitancy continues to be at high levels in the United States. In
part, the issue has also become politicized, especially since the presidential
election in November. Understanding vaccine hesitancy during this period in the
context of social media, including Twitter, can provide valuable guidance both
to computational social scientists and policy makers. Rather than studying a
single Twitter corpus, this paper takes a novel view of the problem by
comparatively studying two Twitter datasets collected between two different
time periods (one before the election, and the other, a few months after) using
the same, carefully controlled data collection and filtering methodology. Our
results show that there was a significant shift in discussion from politics to
COVID-19 vaccines from fall of 2020 to spring of 2021. By using clustering and
machine learning-based methods in conjunction with sampling and qualitative
analysis, we uncover several fine-grained reasons for vaccine hesitancy, some
of which have become more (or less) important over time. Our results also
underscore the intense polarization and politicization of this issue over the
last year.
