Evidence map›Paper›PMID 34341121›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Examining the consumption of radical content on YouTube.

Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset, Markus Mobius, David M Rothschild, Duncan J Watts

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  8. Article
  9. Article
  10. Social Drivers and Algorithmic Mechanisms on Digital Media.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024
    Review
  11. Article
  12. Review
  13. Causally estimating the effect of YouTube's recommender system using counterfactual bots.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  14. Auditing YouTube's recommendation system for ideologically congenial, extreme, and problematic recommendations.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Homa HosseinmardiDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104; homahm@seas.upenn.edu djwatts@seas.upenn.edu.
Amir GhasemianDepartment of Statistics, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-3515-3504
Aaron ClausetDepartment of Computer Science, University of Colorado Boulder, Boulder, CO 80309.ORCID 0000-0002-3529-8746
Markus MobiusMicrosoft Research New England, Cambridge, MA 02142.ORCID 0000-0002-4725-7896
David M RothschildMicrosoft Research New York, New York, NY 10012.ORCID 0000-0002-7792-1989
Duncan J WattsDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104; homahm@seas.upenn.edu djwatts@seas.upenn.edu.ORCID 0000-0001-5005-4961

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, YouTube's scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical "anti-woke" channels, both of which have been claimed to direct attention to radical political content. Here we test this hypothesis using a representative panel of more than 300,000 Americans and their individual-level browsing behavior, on and off YouTube, from January 2016 through December 2019. Using a labeled set of political news channels, we find that news consumption on YouTube is dominated by mainstream and largely centrist sources. Consumers of far-right content, while more engaged than average, represent a small and stable percentage of news consumers. However, consumption of "anti-woke" content, defined in terms of its opposition to progressive intellectual and political agendas, grew steadily in popularity and is correlated with consumption of far-right content off-platform. We find no evidence that engagement with far-right content is caused by YouTube recommendations systematically, nor do we find clear evidence that anti-woke channels serve as a gateway to the far right. Rather, consumption of political content on YouTube appears to reflect individual preferences that extend across the web as a whole.

Indexed as

PoliticsSocial MediaHumansVideo Recordingnews dietonline platformspolitical radicalizationuser behavior

Identifiers

PMID34341121
PMCPMC8364190

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.