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Cited by 13Misinformation and Its ImpactsSocial Media and PoliticsComplex Network Analysis Techniques

How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers

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Kratzke, Nane·2023·Computers
DOI 10.3390/computers12030057

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Abstract

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This 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 identified an echo chamber (red community, 66K accounts) 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 (113K accounts) 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.

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  • Centrality measures identify influential nodes, such as disseminators (high closeness centrality) or bridges (high betweenness centrality), offering insights into individual roles within echo chambers.

    “Centrality measures identify influential nodes, such as disseminators (high closeness centrality) or bridges (high betweenness centrality), offering insights into individual roles within echo chambers [6,111].”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [111]

    Materials and Methods (section 2; page unavailable) says centrality measures can identify critical actors in echo chambers and explicitly describes high-betweenness actors as bridges between groups. The paper mentions closeness centrality but does not identify high-closeness actors as disseminators; section 2.2.3 instead calls high-HITS-hub accounts content disseminators. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Echo chamber detection can occur on individual users, groups, or entire platforms.

    “The difficulties in operationalizing extend to the granularity of echo chamber detection as they can occur on individual users, groups, or entire platforms [111]”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [111]

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Cited by

Sorted by citations

Authors
Kratzke, N.
Affiliations
Technical University of Applied Sciences Lübeck (DE)
Corresponding author
Kratzke, Nane
ORCID
Kratzke, Nane
Venue
Computers
Publisher
MDPI AG
Published
2023-03-03
Volume
12
Issue
3
Pages
57
ISSN
2073-431X (ISSN-L)
DOI
10.3390/computers12030057
OpenAlex
W4323318050
Cited by
13
References
2
Citations by year
2026: 3 · 2025: 7 · 2024: 2 · 2023: 1
FWCI
4.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Social Media and Politics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
Twitter, polarization, modularity optimization, HITS algorithm, disinformation, right-wing populism, community detection

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