Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783662409294020608 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7942992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT guilbeaultdouglas probabilisticsociallearningimprovesthepublicsjudgmentsofnewsveracity AT woolleysamuel probabilisticsociallearningimprovesthepublicsjudgmentsofnewsveracity AT beckerjoshua probabilisticsociallearningimprovesthepublicsjudgmentsofnewsveracity |