Filter Bubbles, Echo Chambers, and Online News Consumption
Open accessAbstract
Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry that they increase ideological segregation. We address the issue by examining web-browsing histories for 50,000 US-located users who regularly read online news. We find that social networks and search engines are associated with an increase in the mean ideological distance between individuals. However, somewhat counterintuitively, these same channels also are associated with an increase in an individual’s exposure to material from his or her less preferred side of the political spectrum. Finally, the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences—both positive and negative—of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the effects is relatively modest.
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There is significant research on the ways in which algorithms produce search results that mirror people's worldviews, but the majority of that work has been carried out on social networks.
“There is significant research on the ways in which algorithms produce search results that mirror people’s worldviews, but the majority of that work has been carried out on social networks [83,84].”
From Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol (2025) · cites this paper as [84]
Web traffic data suggest that relatively few Americans have heavily skewed information diets.
“Web traffic data suggest that relatively few Americans have heavily skewed information diets15–18”
From Like-minded sources on Facebook are prevalent but not polarizing (2023) · cites this paper as [16]
There is a gap in literature: few studies examine the level of use of different media in an integrated way, assuming the hybrid condition of the media system, and analyze affective polarization according to this.
“Pese a que son numerosos los estudios basados en la polarización afectiva y los medios de comunicación (Stroud, 2010;Flaxman et al., 2016;Tsfati & Nir, 2017;Rojo-Martínez et al., 2023;Torcal, 2023), no son tan frecuentes aquellos que estudian el nivel de uso de diferentes medios de forma integrada, asumiendo la condición híbrida del sistema mediático, y analizan según esta el grado de polarización”
From Política, uso de medios y polarización afectiva en España (2024) · cites this paper as Flaxman et al., 2016
El hábito informativo y la exposición a puntos de vista afines vuelven a las sociedades occidentales menos tolerantes con las opiniones opuestas y más extremas, lo que produce una creciente polarización social.
“Esta práctica cotidiana y hábito informativo convertiría a las sociedades occidentales menos tolerantes con aquellos puntos de vista opuestos a los propios (Jamieson y Capella, 2008) y con posiciones u opiniones más extremas, que devienen en una creciente polarización de la sociedad (Flaxman et al., 2016).”
From Homofilia, polarización afectiva y desinformación en Twitter. Caso de estudio sobre la crisis migratoria #Openarms (2021) · cites this paper as Flaxman et al., 2016
The control-condition news selection pattern is consistent with recent observational studies of online web browsing behavior finding that partisans differ in their typical media diet even as they do not completely retreat into echo chambers.
“This is consistent with recent observational studies of online web browsing behavior which find that, with respect to political news, partisans di↵er in their typical media diet even as they do not completely retreat into echo chambers (see e.g., Peterson et al., ND, Flaxman et al., 2016, Guess, 2019).”
From Partisan Gaps in Political Information and Information-Seeking Behavior: Motivated Reasoning or Cheerleading? (2019) · cites this paper as Flaxman et al., 2016
The negative effects of echo chambers in social networks might be overstated.
“This also reaffirms the fact that the negative effects of echo chambers in social networks, which are known to arise due to high levels of homophily in communication, might be overstated”
From Credibility of climate change denial in social media (2019) · cites this paper as Flaxman et al., 2016
Del Vicario et al. and Flaxman et al. demonstrated that content-considering methods effectively detect echo chambers formed through selective content exposure.
“Del Vicario et al. [48] and Flaxman et al. [134] demonstrated that contentconsidering methods effectively detect echo chambers formed through selective content exposure.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [134]
Some studies observe reinforcement of filter bubbles or echo chambers; others provide conflicting evidence in both magnitude and direction.
“Some studies observe reinforcement of this sort (92); others provide conflicting evidence in both magnitude and direction (93-96).”
From Misinformation in and about science (2021) · cites this paper as [94]
Online media has become a widespread source of information and opinion formation.
“With the advent of the Internet, online media has become a widespread source of information and, subsequently, opinion formation5–9.”
From Negativity drives online news consumption (2023) · cites this paper as [5]
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Misinformation, Disinformation, and Online Propaganda
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Measuring online social bubbles
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Algorithmic Effects on the Diversity of Consumption on Spotify
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380281 · Cited by 206 · see citing works
“Various studies have tested this hypothesis, coming to mixed conclusions about whether, and to what extent, recommender systems display a filter bubble effect [2, 10, 21].”
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The Digital Transformation of News Media and the Rise of Disinformation and Fake NewsPreprint
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An Analysis of the Interaction Between Intelligent Software Agents and Human Users
Burr, C., Cristianini, N., & Ladyman, J. (2018) · Minds and Machines · doi:10.1007/s11023-018-9479-0 · Cited by 184 · see citing works
Artificial Intelligence and Democracy: A Conceptual Framework
Jungherr, A. M. (2023) · Social Media + Society · doi:10.1177/20563051231186353 · Cited by 217 · see citing works
Melki, J. P., Tamim, H., Hadid, D., Makki, M. H., Amine, J. E., & Hitti, E. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252830 · Cited by 182 · 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
Mistrust and misinformation: A two-component, socio-epistemic model of belief in conspiracy theories
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Lavazza, A., & Farina, M. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00356 · Cited by 152 · see citing works
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A Confirmation Bias View on Social Media Induced Polarisation During Covid-19
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Facebook news and (de)polarization: reinforcing spirals in the 2016 US election
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Fairwalk: Towards Fair Graph Embedding
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Does online technology make us more or less sociable? A preliminary review and call for research
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Fighting fake news in the COVID-19 era: policy insights from an equilibrium model
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Degenerate Feedback Loops in Recommender Systems
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Rising Tides or Rising Stars?: Dynamics of Shared Attention on Twitter during Media Events
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A critical review of filter bubbles and a comparison with selective exposure
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Partisan selective exposure in online news consumption: evidence from the 2016 presidential campaign
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Self-citation“I Am a Tariff Man”: The Power of Populist Foreign Policy Rhetoric under President Trump
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Children as creators, thinkers and citizens in an AI-driven future
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Right-wing populism, social media and echo chambers in Western democracies
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Fake News on Facebook and Twitter: Investigating How People (Don't) Investigate
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Quantifying Controversy in Social Media
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“This phenomenon has been quantified in many recent studies [3, 11, 14].”
ResultLocal News, Information, and the Nationalization of U.S. Elections
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Brevity Is the Soul of Twitter: The Constraint Affordance and Political Discussion
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Quantifying social organization and political polarization in online platforms
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Cultural Evolution in the Digital Age
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Networks of Audience Overlap in the Consumption of Digital News
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ECONOMETRICS MEETS SENTIMENT: AN OVERVIEW OF METHODOLOGY AND APPLICATIONS
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How Polarized Are Online and Offline News Audiences? A Comparative Analysis of Twelve Countries
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Conversational receptiveness: Improving engagement with opposing views
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Minimizing Polarization and Disagreement in Social Networks
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Analyzing the Impact of Filter Bubbles on Social Network Polarization
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Selective exposure shapes the Facebook news diet
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- Authors
- Flaxman, S., Goel, S., & Rao, J. M.
- Affiliations
- University of Oxford (GB)
- Venue
- Public Opinion Quarterly
- Publisher
- Oxford University Press (OUP)
- Published
- 2016
- Volume
- 80
- Issue
- S1
- Pages
- 298-320
- ISSN
- 0033-362X (ISSN-L)
- 1537-5331
- OpenAlex
- W2303924600
- Cited by
- 1,992
- References
- 54
- Citations by year
- 2026: 166 · 2025: 177 · 2024: 174 · 2023: 213 · 2022: 277 · 2021: 256 · 2020: 251 · 2019: 228 · 2018: 146 · 2017: 66 · 2016: 18 · 2015: 11 · 2014: 5 · 2013: 1 · 2012: 2
- FWCI
- 436.55
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Public Opinion Quarterly (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- filter bubbles, echo chambers, online news consumption, ideological segregation, ideological distance, social networks, search engines, web browsing history, political polarization, information exposure
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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