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.

翻译：虽然现在已有数月多种COVID-19疫苗,但在美国,疫苗仍然处于高水平,部分问题也已经政治化,特别是自11月总统选举以来。在包括Twitter在内的社交媒体背景下了解疫苗的闲置性可以为计算社会科学家和决策者提供宝贵的指导。本文没有研究单一的Twitter内容,而是通过比较研究两个不同时期(选举前一个时期和选举后几个月)之间收集的两个Twitter数据集,使用同样的、经过仔细控制的数据收集和过滤方法,对这一问题有了新的认识。我们的结果显示,从2020年秋季到2021年春季,讨论出现了从政治到COVID-19疫苗的重大转变。通过使用集群和机器学习方法进行抽样和定性分析,我们发现了疫苗闲置性的若干细微原因,其中一些随着时间的推移变得更为重要(或更少 ) 。我们的结果还凸显了这一问题在去年的高度两极分化和政治化。