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The digital spread of misinformation is one of the leading threats todemocracy, public health, and the global economy. Popular strategies formitigating misinformation include crowdsourcing, machine learning, and medialiteracy programs that require social media users to classify news in binaryterms as either true or false. However, research on peer influence suggeststhat framing decisions in binary terms can amplify judgment errors and limitsocial learning, whereas framing decisions in probabilistic terms can reliablyimprove judgments. In this preregistered experiment, we compare online peernetworks that collaboratively evaluate the veracity of news by communicatingeither binary or probabilistic judgments. Exchanging probabilistic estimates ofnews veracity substantially improved individual and group judgments, with theeffect of eliminating polarization in news evaluation. By contrast, exchangingbinary classifications reduced social learning and entrenched polarization. Thebenefits of probabilistic social learning are robust to participants'education, gender, race, income, religion, and partisanship.

Probabilistic Social Learning Improves the Public's Detection of Misinformation