Twitter vaccine misinformation data

Anti-vaccine content is rapidly propagated via social media, fostering
vaccine hesitancy, while pro-vaccine content has not replicated the
opponent's successes. Despite this disparity in the dissemination of
anti- and pro-vaccine posts, linguistic features that facilitate or
inhibit the propagation of vaccine-related content remain less known.
Moreover, most prior machine-learning algorithms classified social-media
posts into binary categories (e.g., misinformation or not) and have rarely
tackled a higher-order classification task based on divergent perspectives
about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our
objectives are (1) to identify sets of linguistic features that facilitate
and inhibit the propagation of vaccine-related content and (2) to compare
whether anti-vaccine, pro-vaccine, and neutral tweets contain either set
more frequently than the others. To achieve these goals, we collected a
large set of social media posts (over 120 million tweets) between Nov. 15
and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage
framework was developed using a fine-tuned BERT classifier, demonstrating
over 99 and 80 percent accuracy for binary and ternary classification.
Finally, the Linguistic Inquiry Word Count text analysis tool was used to
count linguistic features in each classified tweet. Our regression results
show that anti-vaccine tweets are propagated (i.e., retweeted), while
pro-vaccine tweets garner passive endorsements (i.e., favorited). Our
results also yielded the two sets of linguistic features as facilitators
and inhibitors of the propagation of vaccine-related tweets. Finally, our
regression results show that anti-vaccine tweets tend to use the
facilitators, while pro-vaccine counterparts employ the inhibitors. These
findings and algorithms from this study will aid public health
officials' efforts to counteract vaccine misinformation, thereby
facilitating the delivery of preventive measures during pandemics and
epidemics.