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High level of correspondence across different news domain quality rating sets
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500312/ https://www.ncbi.nlm.nih.gov/pubmed/37719749 http://dx.doi.org/10.1093/pnasnexus/pgad286 |
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author | Lin, Hause Lasser, Jana Lewandowsky, Stephan Cole, Rocky Gully, Andrew Rand, David G Pennycook, Gordon |
author_facet | Lin, Hause Lasser, Jana Lewandowsky, Stephan Cole, Rocky Gully, Andrew Rand, David G Pennycook, Gordon |
author_sort | Lin, Hause |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10500312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105003122023-09-15 High level of correspondence across different news domain quality rating sets Lin, Hause Lasser, Jana Lewandowsky, Stephan Cole, Rocky Gully, Andrew Rand, David G Pennycook, Gordon PNAS Nexus Social and Political Sciences 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. Oxford University Press 2023-09-02 /pmc/articles/PMC10500312/ /pubmed/37719749 http://dx.doi.org/10.1093/pnasnexus/pgad286 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Social and Political Sciences Lin, Hause Lasser, Jana Lewandowsky, Stephan Cole, Rocky Gully, Andrew Rand, David G Pennycook, Gordon High level of correspondence across different news domain quality rating sets |
title | High level of correspondence across different news domain quality rating sets |
title_full | High level of correspondence across different news domain quality rating sets |
title_fullStr | High level of correspondence across different news domain quality rating sets |
title_full_unstemmed | High level of correspondence across different news domain quality rating sets |
title_short | High level of correspondence across different news domain quality rating sets |
title_sort | high level of correspondence across different news domain quality rating sets |
topic | Social and Political Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500312/ https://www.ncbi.nlm.nih.gov/pubmed/37719749 http://dx.doi.org/10.1093/pnasnexus/pgad286 |
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