Probabilistic Social Learning Improves the Public's Detection of Misinformation

TL;DR

This study demonstrates that probabilistic social learning enhances the accuracy of misinformation detection and reduces polarization in online peer networks, outperforming binary judgment approaches.

Contribution

It provides empirical evidence that probabilistic framing in social learning improves misinformation detection and mitigates polarization compared to binary judgments.

Findings

Probabilistic judgments significantly improve news veracity assessments.

Binary classifications reduce social learning and increase polarization.

Probabilistic social learning benefits are consistent across diverse demographic groups.

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 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.