The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Gong, Y., Schroeder, A., Pan, B., Sundar, S. S., & Mowen, A. J. (2023) · Information Technology & Tourism · doi:10.1007/s40558-023-00279-4 · Cited by 25
Metadata
- 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
- 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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