论文标题
在Twitter上查找有关Covid-19的大流行的类似人的群集
Finding Clusters of Similar-minded People on Twitter Regarding the Covid-19 Pandemic
论文作者
论文摘要
两种聚类方法将在本文中介绍对COVID-19的大流行和德国相关的公开辩论的用户的两种类似意见的用户。我们相信，他们可以帮助获得对相似志趣相投的团体的概述，并可以支持预防假新闻分布。第一种方法使用一种新方法来创建一个基于用户与最转推用户，即所谓影响者之间的转发关系的网络。第二种方法从用户帖子中提取主题标签来创建“用户功能向量”，然后使用基于先前工作的共识矩阵聚类，以使用相同的语言识别组。通过两种方法，都可以识别出似乎适合德国不同公众意见的集群。但是，我们还发现，由于两种方法中的过滤步骤，来自一种方法的簇与另一个方法的簇无法关联。
Two clustering methods to determine users with similar opinions on the Covid-19 pandemic and the related public debate in Germany will be presented in this paper. We believe, they can help gaining an overview over similar-minded groups and could support the prevention of fake-news distribution. The first method uses a new approach to create a network based on retweet relationships between users and the most retweeted users, the so-called influencers. The second method extracts hashtags from users posts to create a "user feature vector" which is then clustered, using a consensus matrix based on previous work, to identify groups using the same language. With both approaches it was possible to identify clusters that seem to fit groups of different public opinions in Germany. However, we also found that clusters from one approach can not be associated with clusters from the other due to filtering steps in the two methods.
