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

Guilbeault, D; Woolley, S; Becker, J; (2021) Probabilistic social learning improves the public's judgments of news veracity. PLOS ONE , 16 (3) , Article e0247487. 10.1371/journal.pone.0247487. Green open access

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Abstract

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.

Type: Article
Title: Probabilistic social learning improves the public's judgments of news veracity
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0247487
Publisher version: https://doi.org/10.1371/journal.pone.0247487
Language: English
Additional information: Copyright © 2021 Guilbeault et al. 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.
Keywords: Social communication, Human learning, Machine learning, Social media, Learning, Intelligence, Vaccines, Terrorism
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: https://discovery.ucl.ac.uk/id/eprint/10133754
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