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The COVID-19 pandemic has produced a flurry of online activity on socialmedia sites. As such, analysis of social media data during the COVID-19pandemic can produce unique insights into discussion topics and how thosetopics evolve over the course of the pandemic. In this study, we proposeanalyzing discussion topics on Twitter by clustering hashtags. In order toobtain high-quality clusters of the Twitter hashtags, we also propose a novelmulti-view clustering technique that incorporates multiple different data typesthat can be used to describe how users interact with hashtags. The results ofour multi-view clustering show that there are distinct temporal and topicaltrends present within COVID-19 twitter discussion. In particular, we find thatsome topical clusters of hashtags shift over the course of the pandemic, whileothers are persistent throughout, and that there are distinct temporal trendsin hashtag usage. This study is the first to use multi-view clustering toanalyze hashtags and the first analysis of the greater trends of discussionoccurring online during the COVID-19 pandemic.

Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clustering