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.

中文翻译：

---

通过多视图聚类来表征2020年COVID-19大流行期间Twitter上的＃标签使用社区。

COVID-19大流行在社交媒体网站上引发了一系列在线活动。这样，在COVID-19大流行期间对社交媒体数据的分析可以产生对讨论主题以及这些主题在大流行过程中如何演变的独特见解。在本研究中，我们建议通过将主题标签聚类来分析Twitter上的讨论主题。为了获得Twitter标签的高质量群集，我们还提出了一种新颖的多视图群集技术，该技术结合了多种不同的数据类型，可用于描述用户如何与标签交互。我们的多视图聚类结果表明，在COVID-19 Twitter讨论中存在明显的时间和主题趋势。特别是，我们发现一些主题标签标签在大流行过程中发生了变化，其他则始终保持不变，并且在标签使用方面存在明显的时间趋势。这项研究是第一个使用多视图聚类分析标签的方法，也是第一个分析COVID-19大流行期间在线讨论的更大趋势的分析。