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

fact-checking journalism standards misinformation news quality

Journal

PNAS nexus
ISSN: 2752-6542
Titre abrégé: PNAS Nexus
Pays: England
ID NLM: 9918367777906676

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 21 11 2022
accepted: 24 08 2023
medline: 18 9 2023
pubmed: 18 9 2023
entrez: 18 9 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37719749
doi: 10.1093/pnasnexus/pgad286
pii: pgad286
pmc: PMC10500312
doi:

Types de publication

News

Langues

eng

Pagination

pgad286

Subventions

Organisme : European Research Council
ID : 101020961
Pays : International

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.

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Auteurs

Hause Lin (H)

Hill/Levene Schools of Business, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.
Sloan School of Management, Massachusetts Institute of Technology, 100 Main St, Cambridge, MA 02142, USA.
Department of Psychology, Cornell University, Uris Hall, 211, Tower Rd, Ithaca, NY 14853, USA.

Jana Lasser (J)

Institute for Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria.
Complexity Science Hub Vienna, Josefstädterstraße 39, 1080 Vienna, Austria.

Stephan Lewandowsky (S)

School of Psychological Science, University of Bristol, 12a, Priory Road, Bristol BS8 1TU, UK.
School of Psychology, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia.

Rocky Cole (R)

Jigsaw (Google LLC), 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.

Andrew Gully (A)

Jigsaw (Google LLC), 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.

David G Rand (DG)

Sloan School of Management, Massachusetts Institute of Technology, 100 Main St, Cambridge, MA 02142, USA.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, MA 02139, USA.

Gordon Pennycook (G)

Hill/Levene Schools of Business, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.
Department of Psychology, Cornell University, Uris Hall, 211, Tower Rd, Ithaca, NY 14853, USA.
Department of Psychology, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.

Classifications MeSH