  The COVID-19 pandemic has produced a flurry of online activity on social
media sites. As such, analysis of social media data during the COVID-19
pandemic can produce unique insights into discussion topics and how those
topics evolve over the course of the pandemic. In this study, we propose
analyzing discussion topics on Twitter by clustering hashtags. In order to
obtain high-quality clusters of the Twitter hashtags, we also propose a novel
multi-view clustering technique that incorporates multiple different data types
that can be used to describe how users interact with hashtags. The results of
our multi-view clustering show that there are distinct temporal and topical
trends present within COVID-19 twitter discussion. In particular, we find that
some topical clusters of hashtags shift over the course of the pandemic, while
others are persistent throughout, and that there are distinct temporal trends
in hashtag usage. This study is the first to use multi-view clustering to
analyze hashtags and the first analysis of the greater trends of discussion
occurring online during the COVID-19 pandemic.
