Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance.

The primary goal of this study is to examine the association between vaccine rhetoric on Twitter and the public's uptake rates of COVID-19 vaccines in the United States, compared to the extent of an association between self-reported vaccine acceptance and the CDC's uptake rates. We downloaded vaccine-related posts on Twitter in real-time daily for 13 months, from October 2021 to September 2022, collecting over half a billion tweets. A previously validated deep-learning algorithm was then applied to (1) filter out irrelevant tweets and (2) group the remaining relevant tweets into pro-, anti-, and neutral vaccine sentiments. Our results indicate that the tweet counts (combining all three sentiments) were significantly correlated with the uptake rates of all stages of COVID-19 shots (p < 0.01). The self-reported level of vaccine acceptance was not correlated with any of the stages of COVID-19 shots (p > 0.05) but with the daily new infection counts. These results suggest that although social media posts on vaccines may not represent the public's opinions, they are aligned with the public's behaviors of accepting vaccines, which is an essential step for developing interventions to increase the uptake rates. In contrast, self-reported vaccine acceptance represents the public's opinions, but these were not correlated with the behaviors of accepting vaccines. These outcomes provide empirical support for the validity of social media analytics for gauging the public's vaccination behaviors and understanding a nuanced perspective of the public's vaccine sentiment for health emergencies.

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media.

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, provaccine, 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.