preprints.org > computer science and mathematics > information systems > doi: 10.20944/preprints202302.0032.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 February 2023 / Approved: 2 February 2023 / Online: 2 February 2023 (06:54:35 CET)

Background: This study presents a graph-based and purely structural analysis to detect echo chambers on Twitter. Echo chambers are a concern as they can spread misinformation and reinforce harmful stereotypes and biases in social networks. Methods: The study recorded the German-language Twitter stream over two months, recording about 180.000 accounts and their interactions. The study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach was able to identify an echo chamber (red community) that was focused on a few topics with a triad of Anti-Covid, right-wing populism, and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community was much more heterogeneous and showed "normal" communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

Social network; Twitter; Structural analysis; Echo chamber; Detection; Case study; German language; Disinformation

Computer Science and Mathematics, Information Systems

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