Can we blame social media for polarization? Counter-evidence against filter bubble claims during the COVID-19 pandemic
Abstract
Although collective efforts are essential to fight COVID-19, public opinion in the United States is sharply divided by partisan attitudes and health beliefs. Addressing the concern that media use facilitates polarization, this study investigated whether social and traditional media use for COVID-19 information attenuates or reinforces existing disparities. This article focuses on two important areas where the public is highly polarized: partisan affect and vaccine attitudes. Contradicting the filter bubble claim, our survey ( n = 1106) revealed that social media use made people less polarized in both partisan affect and vaccine hesitancy. In contrast, traditional media use made people more polarized in partisan affect. These findings corroborate the growing evidence that social media provide diverse viewpoints and incidental learning.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Greater media diversity reduces the likelihood of echo chamber formation.
“Greater media diversity reduces the likelihood of echo chamber formation, as studies such as Boulianne et al. [16], Dubois et al. [65], Jones-Jang and Chung [68], Burnett et al. [69], found only minimal indication of echo chambers when examining the complete multimedia environment.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [68]
Cites
The Origins and Consequences of Affective Polarization in the United States
Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019) · Annual Review of Political Science · doi:10.1146/annurev-polisci-051117-073034 · Cited by 3,317 · see citing works
Uncivil Agreement: How Politics Became Our Identity
Mason, L. (2018) · doi:10.7208/chicago/9780226524689.001.0001 · Cited by 1,985 · cited 1× · see citing works
“Party identification, typically categorized as Democrat or Republican in the United States, has functioned as a key group identity associated with a host of attitudinal and behavioral consequences (Iyengar et al., 2019; Mason, 2018).”
BackgroundYarchi, M., Baden, C., & Kligler-Vilenchik, N. (2021) · Political Communication · doi:10.1080/10584609.2020.1785067 · Cited by 437 · see citing works
Salmon, D. A., Dudley, M. Z., Glanz, J. M., & Omer, S. B. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.09.035 · Cited by 414 · see citing works
Cho, J., Ahmed, S., Hilbert, M., Liu, B., & Luu, J. (2020) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2020.1757365 · Cited by 164 · cited 1× · see citing works
“For example, a recent experiment found that YouTube algorithms recommend videos that reinforce viewers’ existing political opinion (Cho et al., 2020).”
Background
Flagged first, then by citations
Cited by
Sorted by citations
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
Cuéllar Rivero, R. (2024) · Revista Española de Ciencia Política · doi:10.21308/recp.64.07 · Cited by 3 · see citing works
- Authors
- Jones-Jang, S. M., & Chung, M.
- Affiliations
- Boston College (US)
- Northeastern University (US)
- Corresponding author
- Jones-Jang, S Mo
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2022-06-13
- Volume
- 26
- Issue
- 6
- Pages
- 3370-3389
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W4282572679
- Cited by
- 43
- References
- 5
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 12 · 2023: 10
- FWCI
- 11.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New Media & Society (publishedVersion)
- Topics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- social media, political polarization, filter bubbles, COVID-19 pandemic, partisan affect, vaccine hesitancy, traditional media, media use, health beliefs, incidental learning
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 ›
Are you an author, or do you see a problem with this page? Request a review
