Measuring online social bubbles
Open accessAbstract
Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information.
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Users tend to consume more content from like-minded people.
“Since users tend to consume more content from like-minded people, such consumption biases were linked to the creation of filter bubbles (Nikolov et al., 2015).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Nikolov et al., 2015
Content production biases may impact users' exposure to a variety of information types.
“and may also impact users' exposure to a variety of information types”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Nikolov et al., 2015
Such consumption biases were linked to the creation of filter bubbles.
“Since users tend to consume more content from like-minded people, such consumption biases were linked to the creation of filter bubbles (Nikolov et al., 2015).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Nikolov et al., 2015
Cites
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
Partisan asymmetries in online political activity
Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012) · EPJ Data Science · doi:10.1140/epjds6 · Cited by 234 · see citing works
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The echo chamber effect on social media
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023301118 · Cited by 2,092 · see citing works
The echo chamber is overstated: the moderating effect of political interest and diverse media
Dubois, E., & Blank, G. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1428656 · Cited by 1,055 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“Since users tend to consume more content from like-minded people, such consumption biases were linked to the creation of filter bubbles (Nikolov et al., 2015).”
BackgroundFalse Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289 · see citing works
Social influence and unfollowing accelerate the emergence of echo chambers
Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00084-7 · Cited by 211 · see citing works
Self-citationMe, My Echo Chamber, and I: Introspection on Social Media Polarization
Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186130 · Cited by 117 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Algorithmic audiencing: Why we need to rethink free speech on social media
Riemer, K., & Peter, S. (2021) · Journal of Information Technology · doi:10.1177/02683962211013358 · Cited by 66 · see citing works
Me, My Echo Chamber, and I: Introspection on Social Media Polarization
Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., & Roy, D. (2018) · arXiv · doi:10.48550/arxiv.1803.01731 · Cited by 33 · see citing works
- Authors
- Nikolov, D., Oliveira, D. F. M., Flammini, A., & Menczer, F.
- Affiliations
- Indiana University Bloomington (US)
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2015-12-02
- Volume
- 1
- Pages
- e38
- ISSN
- 2376-5992 (ISSN-L)
- OpenAlex
- W2167366384
- Cited by
- 205
- References
- 2
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 15 · 2023: 36 · 2022: 19 · 2021: 29 · 2020: 21 · 2019: 17 · 2018: 23 · 2017: 19 · 2016: 7
- FWCI
- 54.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ Computer Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- James S. McDonnell Foundation
- National Science Foundation (CCF-1101743)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- social bubbles, echo chambers, information diversity, social media, algorithmic filtering, information exposure, Twitter, search logs, email, information filtering
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