Birds of a Feather Get Recommended Together: Algorithmic Homophily in YouTube’s Channel Recommendations in the United States and Germany
Open accessAbstract
Algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recommendations on YouTube to map the communities that the social network is creating through its algorithms and to test the network for homophily, that is, the connectedness between communities. We find that YouTube’s channel recommendation algorithm fosters the creation of highly homophilous communities in the United States ( n = 13,529 channels) and in Germany ( n = 8,000 channels). Factors that seem to drive YouTube’s recommendations are topics, language, and location. We highlight the issue of homophilous communities in the context of politics where YouTube’s algorithms create far-right communities in both countries.
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YouTube's recommendations amplify far-right content, fostering echo chambers.
“while YouTube's recommendations amplify far-right content, fostering echo chambers [60,86].”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [86]
EI-Index calculates relative homophily, capturing the balance between in-group and out-group ties.
“EI-Index: Calculates relative homophily, capturing the balance between in-group and out-group ties [86,114].”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [86]
Cites
The Matthew Effect in Science: The reward and communication systems of science are considered.
Merton, R. K. (1968) · Science · doi:10.1126/science.159.3810.56 · Cited by 6,720 · 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
Should we worry about filter bubbles?
Zuiderveen Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., de Vreese, C. H., & Helberger, N. (2016) · Internet Policy Review · doi:10.14763/2016.1.401 · Cited by 541 · cited 2× · see citing works
“Consequently, most scholars investigating the filter bubbles have analyzed news recommendations or media diets (e.g., Zuiderveen Borgesius et al., 2016).”
BackgroundBruns, A. (2019) · Internet Policy Review · doi:10.14763/2019.4.1426 · Cited by 193 · cited 1× · see citing works
“This fear is closely related to what Pariser (2011) called the “filter bub-ble”: an algorithm-driven process that would encapsulate each internet user in their own personalized bubble.”
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Algorithm-mediated social learning in online social networks
Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.06.008 · Cited by 132 · see citing works
Inoculating against the spread of Islamophobic and radical-Islamist disinformation
Lewandowsky, S., & Yesilada, M. (2021) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-021-00323-z · Cited by 62 · 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
The chat-chamber effect: Trusting the AI hallucination
Jacob, C., Kerrigan, P., & Bastos, M. (2025) · Big Data & Society · doi:10.1177/20539517241306345 · Cited by 22 · see citing works
- Authors
- Kaiser, J., & Rauchfleisch, A.
- Affiliations
- Harvard University (US)
- National Taiwan University (TW)
- Corresponding author
- Kaiser, Jonas
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2020-10
- Volume
- 6
- Issue
- 4
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W3108359080
- Cited by
- 74
- References
- 4
- Citations by year
- 2026: 15 · 2025: 10 · 2024: 16 · 2023: 11 · 2022: 15 · 2021: 7
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Social Media + Society (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- european research council (Proposal 648311, 2015-2020)
- open society foundations
- ford foundation
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- YouTube, recommendation algorithms, homophily, network communities, social networks, political polarization, topic similarity, language, location, United States, Germany
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