  Attitudes about vaccination have become more polarized; it is common to see
vaccine disinformation and fringe conspiracy theories online. An observational
study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the
authors analyzed approximately six months' of Twitter discourse -- 1.3 million
original tweets and 18 million retweets between December 2019 and June 2020,
ranging from before to after the establishment of Covid-19 as a pandemic. This
work expands upon Ojea Quintana et al. (2021) with two main contributions from
data science. First, based on the authors' initial network clustering and
qualitative analysis techniques, we are able to clearly demarcate and visualize
the language patterns used in discourse by Antivaxxers (anti-vaccination
campaigners and vaccine deniers) versus other clusters (collectively, Others).
Second, using the characteristics of Antivaxxers' tweets, we develop text
classifiers to determine the likelihood a given user is employing
anti-vaccination language, ultimately contributing to an early-warning
mechanism to improve the health of our epistemic environment and bolster (and
not hinder) public health initiatives.
