Cited by 164Social Media and PoliticsMedia Influence and PoliticsMisinformation and Its Impacts

The DOI matches its Crossref record; 62 of the 63 references it cites were found in a registry or an index, and 1 are not checked yet; 7 of the 54 sources its claims cite were checked for support.

Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization

Open access
Cho, Jaeho, Ahmed, Saifuddin, Hilbert, Martin, Liu, Billy, Luu, Jonathan·2020·Journal of Broadcasting & Electronic Media

Abstract

This study examines algorithm effects on user opinion, utilizing a real-world recommender algorithm of a highly popular video-sharing platform, YouTube. We experimentally manipulate user search/watch history by our custom programming. A controlled laboratory experiment is then conducted to examine whether exposure to algorithmically recommended content reinforces and polarizes political opinions. Results suggest that political self-reinforcement, as indicated by the political emotion-ideology alignment, and affective polarization are heightened by political videos – selected by the YouTube recommender algorithm – based on participants’ own search preferences. Suggestions for how to reduce algorithm-induced political polarization and implications of algorithmic personalization for democracy are discussed.

Verified 2026-10-11 · 1 reference not checked yet

Overview

Integrity signals

Registry

Crossref · 10.1080/08838151.2020.1757365

Cites

62 of 63 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

33 Own interpretation · 19 No full text of the sources · 12 Own result · 10 Own method · 5 Partly supported · 4 Not checked yet · 3 Sources not found · 2 Supported so far · 2 No basis given · accuracy not yet measured

Show all 35 claims

  • It may state two separate claims as one. 24
  • Probably not a substantial claim. 40
  • The quote only partly states this claim. 2
55 of 55
  • The authors state this as an assumption.

    The terms that users enter for their online searches are reflective of what they care about and what they believe.

    “A key assumption is that the terms that users enter for their online searches are reflective of what they care about and what they believe.”
  • The paper relies on this without support.

    Personalization by users' past behavior is a key mechanism of online search and recommender algorithms.

    “Although personalization by users' past behavior is a key mechanism, the complex and dynamic algorithmic systems handling big data make it difficult to predict outcomes (Lazer, 2015).”
  • The paper relies on this without support.

    Algorithmic recommendations are a product of users' search terms and subsequent consumption of information.

    “since algorithmic recommendations are a product of users' search terms and subsequent consumption of information.”
  • The paper relies on this without support.

    The overall body of available information online about the election and candidates was highly polarized at the time when this experiment was conducted.

    “the overall body of available information online about the election and candidates was also likely highly polarized at the time when this experiment was conducted.”
  • The paper relies on this without support.

    The opinion targets (presidential candidates) are much more salient and polarizing in participants' minds than any other issues.

    “It is thus highly likely that the opinion targets (i.e., presidential candidates) are much more salient and polarizing in participants' minds than any other issues.”
  • The paper relies on this without support.

    Online platforms operating in different contexts program and run their algorithms differently.

    “However, online platforms operating in different contexts program and run their algorithms differently.”
  • The paper relies on this without support.

    Online search and recommender algorithms are essentially black boxes.

    “This is why online search and recommender algorithms are essentially black boxes.”
7 of 7

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • ConsistentF(2, 104) = 4.47reported p < .05 · recomputed p = .014

    “F(2, 104) = 4.47, p < .05”

1 of 1

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Show all 54 references

Research works printed but not linked: 4

  1. Fiske S. T., 1986, Handbook of motivation and cognition: Foundations of social behavior, 167
  2. Rosenberg M. J., 1960, Attitude organization and change, 1
  3. Sunstein C. R., 2001, Echo chambers: Bush v. Gore, impeachment, and beyond
  4. Zuiderveen Borghesius F., 2016, Internet Policy Review, 5, 1

Cited by

Sorted by citations

See all 164

Metadata

Authors
Cho, J., Ahmed, S., Hilbert, M., Liu, B., & Luu, J.
Affiliations
  • University of California, Davis (US)
  • Nanyang Technological University (SG)
  • Software Engineer, Sacramento, CA, USA
  • Software Engineer, San Diego, CA, USA
Corresponding author
Cho, Jaeho
Venue
Journal of Broadcasting & Electronic Media
Published
2020-04-27
Volume
64
Issue
2
Pages
150-172
ISSN
  • 0883-8151 (ISSN-L)
  • 1550-6878
OpenAlex
W3018249611
Cited by
164
References
54
Citations by year
2026: 21 · 2025: 33 · 2024: 33 · 2023: 38 · 2022: 20 · 2021: 17 · 2020: 2
FWCI
23.10
Type
journal article
Language
en
Open access
Locations
  • Journal of Broadcasting & Electronic Media (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
algorithmic recommendation, political polarization, YouTube, affective polarization, algorithmic personalization, democracy

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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.

A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

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