On the Efficacy of Accuracy Prompts Across Partisan Lines: An Adversarial Collaboration.

The spread of misinformation is a pressing societal challenge. Prior work shows that shifting attention to accuracy increases the quality of people's news-sharing decisions. However, researchers disagree on whether accuracy-prompt interventions work for U.S. Republicans/conservatives and whether partisanship moderates the effect. In this preregistered adversarial collaboration, we tested this question using a multiverse meta-analysis (k = 21; N = 27,828). In all 70 models, accuracy prompts improved sharing discernment among Republicans/conservatives. We observed significant partisan moderation for single-headline "evaluation" treatments (a critical test for one research team) such that the effect was stronger among Democrats than Republicans. However, this moderation was not consistently robust across different operationalizations of ideology/partisanship, exclusion criteria, or treatment type. Overall, we observed significant partisan moderation in 50% of specifications (all of which were considered critical for the other team). We discuss the conditions under which moderation is observed and offer interpretations.

Crowds Can Effectively Identify Misinformation at Scale.

Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.

Misinformation warning labels are widely effective: A review of warning effects and their moderating features.

There is growing concern over the spread of misinformation online. One widely adopted intervention by platforms for addressing falsehoods is applying "warning labels" to posts deemed inaccurate by fact-checkers. Despite a rich literature on correcting misinformation after exposure, much less work has examined the effectiveness of warning labels presented concurrent with exposure. Promisingly, existing research suggests that warning labels effectively reduce belief and spread of misinformation. The size of these beneficial effects depends on how the labels are implemented and the characteristics of the content being labeled. Despite some individual differences, recent evidence indicates that warning labels are generally effective across party lines and other demographic characteristics. We discuss potential implications and limitations of labeling policies for addressing online misinformation.

Deviancy Aversion and Social Norms.

We propose that deviancy aversion-people's domain-general discomfort toward the distortion of patterns (repeated forms or models)-contributes to the strength and prevalence of social norms in society. Five studies (N = 2,390) supported this hypothesis. In Study 1, individuals' deviancy aversion, for instance, their aversion toward broken patterns of simple geometric shapes, predicted negative affect toward norm violations (affect), greater self-reported norm following (behavior), and judging norms as more valuable (belief). Supporting generalizability, deviancy aversion additionally predicted greater conformity on accuracy-orientated estimation tasks (Study 2), adherence to physical distancing norms during COVID-19 (Study 3), and increased following of fairness norms (Study 4). Finally, experimentally heightening deviancy aversion increased participants' negative affect toward norm violations and self-reported norm behavior, but did not convincingly heighten belief-based norm judgments (Study 5). We conclude that a human sensitivity to pattern distortion functions as a low-level affective process that promotes and maintains social norms in society.

Shared partisanship dramatically increases social tie formation in a Twitter field experiment.

Americans are much more likely to be socially connected to copartisans, both in daily life and on social media. However, this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot's strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from copartisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting and have important implications for political psychology, social media, and the politically polarized state of the American public.

Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic.

Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties' COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens' responses to COVID-19 are subject to a deep-and consequential-partisan divide.

Reliance on emotion promotes belief in fake news.

What is the role of emotion in susceptibility to believing fake news? Prior work on the psychology of misinformation has focused primarily on the extent to which reason and deliberation hinder versus help the formation of accurate beliefs. Several studies have suggested that people who engage in more reasoning are less likely to fall for fake news. However, the role of reliance on emotion in belief in fake news remains unclear. To shed light on this issue, we explored the relationship between experiencing specific emotions and believing fake news (Study 1; N = 409). We found that across a wide range of specific emotions, heightened emotionality at the outset of the study was predictive of greater belief in fake (but not real) news posts. Then, in Study 2, we measured and manipulated reliance on emotion versus reason across four experiments (total N = 3884). We found both correlational and causal evidence that reliance on emotion increases belief in fake news: self-reported use of emotion was positively associated with belief in fake (but not real) news, and inducing reliance on emotion resulted in greater belief in fake (but not real) news stories compared to a control or to inducing reliance on reason. These results shed light on the unique role that emotional processing may play in susceptibility to fake news.

Autism spectrum traits predict higher social psychological skill.

Social-cognitive skills can take different forms, from accurately predicting individuals' intentions, emotions, and thoughts (person perception or folk psychology) to accurately predicting social phenomena more generally. Past research has linked autism spectrum (AS) traits to person perception deficits in the general population. We tested whether AS traits also predict poor accuracy in terms of predicting generalized social phenomena, assessed via participants' accuracy at predicting social psychological phenomena (e.g., social loafing, social projection, group think). We found the opposite. In a sample of ∼6,500 participants in 104 countries, AS traits predicted slightly higher social psychological skill. A second study with 400 participants suggested that heightened systemizing underlies this relationship. Our results indicate that AS traits relate positively to a form of social cognitive skill-predicting social psychological phenomena-and highlight the importance of distinguishing between divergent types of social cognition.