A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes

David Hartmann (), Sonja Mei Wang (), Lena Pohlmann () and Bettina Berendt ()
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David Hartmann: Technische Universität Berlin
Sonja Mei Wang: University of Wuppertal
Lena Pohlmann: Technische Universität Berlin
Bettina Berendt: Technische Universität Berlin

Journal of Computational Social Science, 2025, vol. 8, issue 2, No 26, 59 pages

Abstract: Abstract This systematic review synthesizes research on echo chambers and filter bubbles to explore the reasons behind dissent regarding their existence, antecedents, and effects. It provides a taxonomy of conceptualizations and operationalizations, analyzing how measurement approaches and contextual factors influence outcomes. The review of 129 studies identifies variations in measurement approaches, as well as regional, political, cultural, and platform-specific biases, as key factors contributing to the lack of consensus. Studies based on homophily and computational social science methods often support the echo chamber hypothesis, while research on content exposure and broader media environments, such as surveys, tends to challenge it. Group behavior, cultural influences, instant messaging platforms, and short video platforms remain underexplored. The strong geographic focus on the United States further highlights the need for studies in multi-party systems and regions beyond the Global North. Future research should prioritize cross-platform studies, continuous algorithmic audits, and investigations into the causal links between polarization, fragmentation, and echo chambers to advance the field. This review also provides recommendations for using the EU’s Digital Services Act to enhance research in this area and conduct studies outside the US in multi-party systems. By addressing these gaps, this review contributes to a more comprehensive understanding of echo chambers, their measurement, and their societal impacts.

Keywords: Computational social science; Systematic review; Echo chamber; Filter bubble; Measurement modeling; DSA (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00381-z

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