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Probabilistic social learning improves the public’s judgments of news veracity

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary ter...

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Detalles Bibliográficos
Autores principales: Guilbeault, Douglas, Woolley, Samuel, Becker, Joshua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942992/
https://www.ncbi.nlm.nih.gov/pubmed/33690668
http://dx.doi.org/10.1371/journal.pone.0247487
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author Guilbeault, Douglas
Woolley, Samuel
Becker, Joshua
author_facet Guilbeault, Douglas
Woolley, Samuel
Becker, Joshua
author_sort Guilbeault, Douglas
collection PubMed
description The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants’ education, gender, race, income, religion, and partisanship.
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spelling pubmed-79429922021-03-19 Probabilistic social learning improves the public’s judgments of news veracity Guilbeault, Douglas Woolley, Samuel Becker, Joshua PLoS One Research Article The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants’ education, gender, race, income, religion, and partisanship. Public Library of Science 2021-03-09 /pmc/articles/PMC7942992/ /pubmed/33690668 http://dx.doi.org/10.1371/journal.pone.0247487 Text en © 2021 Guilbeault et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Guilbeault, Douglas
Woolley, Samuel
Becker, Joshua
Probabilistic social learning improves the public’s judgments of news veracity
title Probabilistic social learning improves the public’s judgments of news veracity
title_full Probabilistic social learning improves the public’s judgments of news veracity
title_fullStr Probabilistic social learning improves the public’s judgments of news veracity
title_full_unstemmed Probabilistic social learning improves the public’s judgments of news veracity
title_short Probabilistic social learning improves the public’s judgments of news veracity
title_sort probabilistic social learning improves the public’s judgments of news veracity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942992/
https://www.ncbi.nlm.nih.gov/pubmed/33690668
http://dx.doi.org/10.1371/journal.pone.0247487
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