论文标题
通过多视图集群在2020年Covid-19期间在Twitter上表征标签使用的社区
Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clustering
论文作者
论文摘要
COVID-19大流行在社交媒体网站上产生了一系列的在线活动。因此，对COVID-19大流行期间社交媒体数据的分析可以产生对讨论主题的独特见解，以及这些主题如何在大流行过程中发展。在这项研究中，我们建议通过群集主题标签在Twitter上分析讨论主题。为了获得Twitter主题标签的高质量簇，我们还提出了一种新颖的多视图聚类技术，该技术包含了多种不同的数据类型，可用于描述用户如何与主题标签进行交互。我们的多视图聚类的结果表明，Covid-19 Twitter讨论中存在明显的时间和局部趋势。特别是，我们发现主题标签的一些主题簇在整个大流行过程中都发生了变化，而其他主题标签则始终存在，并且主题标签的使用情况有明显的时间趋势。这项研究是第一个使用多视图聚类来分析主题标签的一项，也是对Covid-19-19大流行期间在线讨论趋势的首次分析。
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.
