Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 18, November 2021

Finding Clusters of Similar-minded People on Twitter Regarding the Covid-19 Pandemic


Philipp Kappus and Paul Groß, Baden-Wuerttemberg Cooperative State University, Germany


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 helpgaining 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 retweetrelationships 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 cannot be associated with clusters from the other due to filtering steps in the two methods.


Data Analysis, Twitter, Covid-19, Retweet network, Hashtags.