Understanding COVID-19 Vaccine Reaction through Comparative Analysis on Twitter
This provides insights for computational social scientists and policymakers on vaccine hesitancy dynamics, though it is incremental as it applies existing methods to new time periods.
The paper tackled understanding COVID-19 vaccine hesitancy by comparatively analyzing Twitter data from before and after the 2020 U.S. presidential election, revealing a significant shift in discussion from politics to vaccines and uncovering fine-grained reasons for hesitancy that changed over time.
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.