German Corona Protest Mobilizers on Telegram and Their Relations to the Far Right: A Network and Topic Analysis

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Abstract

The Querdenken movement, the leading force behind German corona protests, is suspected of being a gateway to far-right attitudes due to radicalizing inward-oriented communication on Telegram. To investigate potential connections of this movement to the far right and alternative media—and to explore key topics of the Querdenken network over time—we analyzed 6,294,955 messages from 578 public Telegram channels via network analysis and structural topic modeling. This analysis revealed that Querdenken’s subcommunities preferably forward content from far-right and QAnon communities, while far-right and conspiracy theorist alternative media channels act as content distributors for the movement. Four main topics appeared in the Querdenken network with varying prevalence over time and across different communities: promotion, QAnon, right-wing populism, and COVID-19 conspiracy theories. Our results highlight potential directions for future research and practical implications, for example, that political decision makers should account for the increasing influence of the QAnon movement on Querdenken mobilizers’ Telegram activity.

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To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.