When politics trumps truth: Political concordance versus veracity as a determinant of believing, sharing, and recalling the news.

Resistance to truth and susceptibility to falsehood threaten democracies around the globe. The present research assesses the magnitude, manifestations, and predictors of these phenomena, while addressing methodological concerns in past research. We conducted a preregistered study with a split-sample design (discovery sample N = 630, validation sample N = 1,100) of U.S. Census-matched online adults. Proponents and opponents of 2020 U.S. presidential candidate Donald Trump were presented with fake and real political headlines ahead of the election. The political concordance of the headlines determined participants' belief in and intention to share news more than the truth of the headlines. This "concordance-over-truth" bias persisted across education levels, analytic reasoning ability, and partisan groups, with some evidence of a stronger effect among Trump supporters. Resistance to true news was stronger than susceptibility to fake news. The most robust predictors of the bias were participants' belief in the relative objectivity of their political side, extreme views about Trump, and the extent of their one-sided media consumption. Interestingly, participants stronger in analytic reasoning, measured with the Cognitive Reflection Task, were more accurate in discerning real from fake headlines when accurate conclusions aligned with their ideology. Finally, participants remembered fake headlines more than real ones regardless of the political concordance of the news story. Discussion explores why the concordance-over-truth bias observed in our study is more pronounced than previous research suggests, and examines its causes, consequences, and potential remedies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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

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.