Examining the interactive effects of the filter bubble and the echo chamber on radicalization
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Recommendation algorithms often suggest accounts aligned with users' ideological profiles.
“Wolfowicz et al. [101] analyzed trace data from ego-centric networks on Twitter/X and found that recommendation algorithms often suggest accounts aligned with users' ideological profiles.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [101]
Weak ties expose users to radical ideas, but the lack of reciprocal connections may limit deeper engagement.
“While these weak ties expose users to radical ideas, the lack of reciprocal connections may limit deeper engagement.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [101]
Cites
The online disinhibition effect
Suler, J. (2004) · CyberPsychology & Behavior · doi:10.1089/1094931041291295 · Cited by 4,375 · see citing works
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992 · see citing works
FILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849 · see citing works
Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982 · cited 1× · see citing works
“In line with these possible explanations, Barberá (2015) found that new Twitter users are more likely to follow more ideologically extreme profiles in the early stages of their network development.”
BackgroundBruns, A. (2019) · Internet Policy Review · doi:10.14763/2019.4.1426 · Cited by 193 · cited 5× · see citing works
“Second, measures of ‘echo chamberness’ often fail to capture the type of network structure characteristics described by the respective propositions (Bruns 2019a).”
BackgroundMethodInfluentialDigital drift and the criminal interaction order
Goldsmith, A., & Brewer, R. (2015) · Theoretical Criminology · doi:10.1177/1362480614538645 · Cited by 112 · cited 1× · see citing works
“As a user’s consumption and exposure become increasingly dominated by radical content, and their associations increasingly made up of similar ties, they may begin on a digital or algorithmic “drift” towards radicalization (Goldsmith and Brewer 2015; Richards and Wood 2020).”
BackgroundHilbert, M., Ahmed, S., Cho, J., Liu, B., & Luu, J. (2018) · Communication Methods and Measures · doi:10.1080/19312458.2018.1479843 · Cited by 49 · see citing works
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A systematic review of worldwide causal and correlational evidence on digital media and democracy
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01460-1 · Cited by 348 · see citing works
Hartmann, D., Wang, S. M., Pohlmann, L., & Berendt, B. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00381-z · Cited by 59 · see citing works
- Authors
- Wolfowicz, M., Weisburd, D., & Hasisi, B.
- Affiliations
- Hebrew University of Jerusalem (IL)
- George Mason University (US)
- Corresponding author
- Wolfowicz, Michael
- Venue
- Journal of Experimental Criminology
- Published
- 2021-08-03
- Volume
- 19
- Issue
- 1
- Pages
- 119-141
- ISSN
- 1572-8315 (ISSN-L)
- 1573-3750
- OpenAlex
- W3188738270
- Cited by
- 54
- References
- 7
- Citations by year
- 2026: 16 · 2025: 14 · 2024: 11 · 2023: 6 · 2022: 6 · 2021: 1
- FWCI
- 7.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Experimental Criminology (publishedVersion)
- Funders
- Horizon 2020 (699824)
- Federmann Cyber Security Research Center (N/A)
- Topics
- Social Sciences › Sociology and Political Science › Terrorism, Counterterrorism, and Political Violence
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- filter bubbles, echo chambers, radicalization, personalization algorithms, Twitter, social learning theory, suicide bombings, justification of violence, network modularity, ego-centric networks, online radicalization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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