Evidence map›Paper›PMID 37719749›Full record

ArticlePNAS nexus2023

High level of correspondence across different news domain quality rating sets.

Hause Lin, Jana Lasser, Stephan Lewandowsky, Rocky Cole, Andrew Gully, David G Rand, Gordon Pennycook

Abstract readNews
In one paragraph

Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Divergent patterns of engagement with partisan and low-quality news across seven social media platforms.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Designing social media content recommendation algorithms for societal good.Annals of the New York Academy of Sciences · 2025
    Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. The consequences of misinformation concern on media consumption.Harvard Kennedy School misinformation review · 2024
    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

7 authors.

Hause LinHill/Levene Schools of Business, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.ORCID https://orcid.org/0000-0003-4590-7039
Jana LasserInstitute for Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria.ORCID https://orcid.org/0000-0002-4274-4580
Stephan LewandowskySchool of Psychological Science, University of Bristol, 12a, Priory Road, Bristol BS8 1TU, UK.ORCID https://orcid.org/0000-0003-1655-2013
Rocky ColeJigsaw (Google LLC), 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.
Andrew GullyJigsaw (Google LLC), 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.
David G RandSloan School of Management, Massachusetts Institute of Technology, 100 Main St, Cambridge, MA 02142, USA.ORCID https://orcid.org/0000-0001-8975-2783
Gordon PennycookHill/Levene Schools of Business, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.ORCID https://orcid.org/0000-0003-1344-6143

Funding

European Research Council 101020961
6 · The paper itself

Abstract

One widely used approach for quantifying misinformation consumption and sharing is to evaluate the quality of the news domains that a user interacts with. However, different media organizations and fact-checkers have produced different sets of news domain quality ratings, raising questions about the reliability of these ratings. In this study, we compared six sets of expert ratings and found that they generally correlated highly with one another. We then created a comprehensive set of domain ratings for use by the research community (github.com/hauselin/domain-quality-ratings), leveraging an ensemble "wisdom of experts" approach. To do so, we performed imputation together with principal component analysis to generate a set of aggregate ratings. The resulting rating set comprises 11,520 domains-the most extensive coverage to date-and correlates well with other rating sets that have more limited coverage. Together, these results suggest that experts generally agree on the relative quality of news domains, and the aggregate ratings that we generate offer a powerful research tool for evaluating the quality of news consumed or shared and the efficacy of misinformation interventions.

Indexed as

fact-checkingjournalism standardsmisinformationnews quality

Identifiers

PMID37719749
PMCPMC10500312

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.