  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 evaluate 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 entrenched polarization. The
benefits of probabilistic social learning are robust to participants'
education, gender, race, income, religion, and partisanship.

Social and Information Networks;Computers and Society