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

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.

Psychological Underpinnings of Misinformation Countermeasures

“…In the case of fact-checking interventions, studies linking intervention effectiveness to theory reference a diverse range of theories. For instance, Bayesian updating (Carnahan et al, 2021), social learning (Guilbeault et al, 2021), and narrative transportation (Huang & Wang, 2022) are cited. This could either indicate that fact-checking subcategories are distinct in their functioning or that scholars chose theories rather arbitrarily to substantiate their interventions.…”

“…In the case of fact-checking interventions, studies linking intervention effectiveness to theory reference a diverse range of theories. For instance, Bayesian updating (Carnahan et al, 2021), social learning (Guilbeault et al, 2021), and narrative transportation (Huang & Wang, 2022)…”

An Online experiment during the 2020 US–Iran crisis shows that exposure to common enemies can increase political polarization

“…Simple survey experiments, however, lack the external validity necessary to demonstrate whether exposure to a common enemy shapes anything more than ephemeral attitudes or dispositions. In this paper, we adopt a hybrid research design in which we recruited a large group of Democrats and Republicans to participate in a social learning task 24 , 26 which we developed using the online platform Empirica 27 to study how different primes about collective identity influence how partisans exchange information to collaboratively solve an estimation problem when financial incentives are at stake.…”