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

TL;DR

This paper introduces a novel multi-view clustering method to analyze Twitter hashtags during COVID-19, revealing temporal and topical trends and persistent discussion themes over the pandemic period.

Contribution

It presents the first application of multi-view clustering to Twitter hashtags and provides new insights into online discussion dynamics during COVID-19.

Findings

Identification of distinct temporal trends in hashtag usage

Discovery of persistent and shifting topical clusters

Revealing the evolution of COVID-19 related discussions

Abstract

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.