Differences in misinformation sharing can lead to politically asymmetric sanctions.

In response to intense pressure, technology companies have enacted policies to combat misinformation1-4. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias5-7. We argue that differential sharing of misinformation by people identifying with different political groups8-15 could lead to political asymmetries in enforcement, even by unbiased policies. We first analysed 9,000 politically active Twitter users during the US 2020 presidential election. Although users estimated to be pro-Trump/conservative were indeed substantially more likely to be suspended than those estimated to be pro-Biden/liberal, users who were pro-Trump/conservative also shared far more links to various sets of low-quality news sites-even when news quality was determined by politically balanced groups of laypeople, or groups of only Republican laypeople-and had higher estimated likelihoods of being bots. We find similar associations between stated or inferred conservatism and low-quality news sharing (on the basis of both expert and politically balanced layperson ratings) in 7 other datasets of sharing from Twitter, Facebook and survey experiments, spanning 2016 to 2023 and including data from 16 different countries. Thus, even under politically neutral anti-misinformation policies, political asymmetries in enforcement should be expected. Political imbalance in enforcement need not imply bias on the part of social media companies implementing anti-misinformation policies.

A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Shifting attention to accuracy can reduce misinformation online.

In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media1-4. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation5-7. Here, we attempt to address both of these questions. First, we find that the veracity of headlines has little effect on sharing intentions, despite having a large effect on judgments of accuracy. This dissociation suggests that sharing does not necessarily indicate belief. Nonetheless, most participants say it is important to share only accurate news. To shed light on this apparent contradiction, we carried out four survey experiments and a field experiment on Twitter; the results show that subtly shifting attention to accuracy increases the quality of news that people subsequently share. Together with additional computational analyses, these findings indicate that people often share misinformation because their attention is focused on factors other than accuracy-and therefore they fail to implement a strongly held preference for accurate sharing. Our results challenge the popular claim that people value partisanship over accuracy8,9, and provide evidence for scalable attention-based interventions that social media platforms could easily implement to counter misinformation online.

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation.

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones-even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate-both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news-and consistent with the predictions of our model-we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.

Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes.

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.

No association between numerical ability and politically motivated reasoning in a large US probability sample.

The highly influential theory of "Motivated System 2 Reasoning" argues that analytical, deliberative ("System 2") reasoning is hijacked by identity when considering ideologically charged issues-leading people who are more likely to engage in such reasoning to be more polarized, rather than more accurate. Here, we fail to replicate the key empirical support for this theory across five contentious issues, using a large gold-standard nationally representative probability sample of Americans. While participants were more accurate in evaluating a contingency table when the outcome aligned with their politics (even when controlling for prior beliefs), we find that participants with higher numeracy were more accurate in evaluating the contingency table, regardless of whether or not the table's outcome aligned with their politics. These findings call for a reconsideration of the effect of identity on analytical reasoning.

Quantifying the potential persuasive returns to political microtargeting.

Much concern has been raised about the power of political microtargeting to sway voters' opinions, influence elections, and undermine democracy. Yet little research has directly estimated the persuasive advantage of microtargeting over alternative campaign strategies. Here, we do so using two studies focused on U.S. policy issue advertising. To implement a microtargeting strategy, we combined machine learning with message pretesting to determine which advertisements to show to which individuals to maximize persuasive impact. Using survey experiments, we then compared the performance of this microtargeting strategy against two other messaging strategies. Overall, we estimate that our microtargeting strategy outperformed these strategies by an average of 70% or more in a context where all of the messages aimed to influence the same policy attitude (Study 1). Notably, however, we found no evidence that targeting messages by more than one covariate yielded additional persuasive gains, and the performance advantage of microtargeting was primarily visible for one of the two policy issues under study. Moreover, when microtargeting was used instead to identify which policy attitudes to target with messaging (Study 2), its advantage was more limited. Taken together, these results suggest that the use of microtargeting-combining message pretesting with machine learning-can potentially increase campaigns' persuasive influence and may not require the collection of vast amounts of personal data to uncover complex interactions between audience characteristics and political messaging. However, the extent to which this approach confers a persuasive advantage over alternative strategies likely depends heavily on context.

Correcting misperceptions of out-partisans decreases American legislators' support for undemocratic practices.

There is substantial concern about democratic backsliding in the United States. Evidence includes notably high levels of animosity toward out-partisans and support for undemocratic practices (SUP) among the general public. Much less is known, however, about the views of elected officials-even though they influence democratic outcomes more directly. In a survey experiment conducted with state legislators (N = 534), we show that these officials exhibit less animosity toward the other party, less SUP, and less support for partisan violence (SPV) than the general public. However, legislators vastly overestimate the levels of animosity, SUP, and SPV among voters from the other party (though not among voters from their own party). Further, those legislators randomly assigned to receive accurate information about the views of voters from the other party reported significantly lower SUP and marginally significantly lower partisan animosity toward the other party. This suggests that legislators' democratic attitudes are causally linked to their perceptions of other-party voters' democratic attitudes. Our findings highlight the importance of ensuring that office holders have access to reliable information about voters from both parties.

Information gerrymandering and undemocratic decisions.

People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks1-3 and distorted by zealots and automated bots4. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an 'influence network' that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock.

Encouraging the resumption of economic activity after COVID-19: Evidence from a large scale-field experiment in China.

As the COVID-19 pandemic comes to an end, governments find themselves facing a new challenge: motivating citizens to resume economic activity. What is an effective way to do so? We investigate this question using a field experiment in the city of Zhengzhou, China, immediately following the end of the city's COVID-19 lockdown. We assessed the effect of a descriptive norms intervention providing information about the proportion of participants' neighbors who have resumed economic activity. We find that informing individuals about their neighbors' plans to visit restaurants increases the fraction of participants visiting restaurants by 12 percentage points (37%), among those participants who underestimated the proportion of neighbors who resumed economic activity. Those who overestimated did not respond by reducing restaurant attendance (the intervention yielded no "boomerang" effect); thus, our descriptive norms intervention yielded a net positive effect. We explore the moderating role of risk preferences and the effect of the intervention on subjects' perceived risk of going to restaurants, as well as the contrast with an intervention for parks, which were already perceived as safe. All of these analyses suggest our intervention worked by reducing the perceived risk of going to restaurants.

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.

Credibility-enhancing displays promote the provision of non-normative public goods.

Promoting the adoption of public goods that are not yet widely accepted is particularly challenging. This is because most tools for increasing cooperation-such as reputation concerns1 and information about social norms2-are typically effective only for behaviours that are commonly practiced, or at least generally agreed upon as being desirable. Here we examine how advocates can successfully promote non-normative (that is, rare or unpopular) public goods. We do so by applying the cultural evolutionary theory of credibility-enhancing displays3, which argues that beliefs are spread more effectively by actions than by words alone-because actions provide information about the actor's true beliefs. Based on this logic, people who themselves engage in a given behaviour will be more effective advocates for that behaviour than people who merely extol its virtues-specifically because engaging in a behaviour credibly signals a belief in its value. As predicted, a field study of a programme that promotes residential solar panel installation in 58 towns in the United States-comprising 1.4 million residents in total-found that community organizers who themselves installed through the programme recruited 62.8% more residents to install solar panels than community organizers who did not. This effect was replicated in three pre-registered randomized survey experiments (total n = 1,805). These experiments also support the theoretical prediction that this effect is specifically driven by subjects' beliefs about what the community organizer believes about solar panels (that is, second-order beliefs), and demonstrate generalizability to four other highly non-normative behaviours. Our findings shed light on how to spread non-normative prosocial behaviours, offer an empirical demonstration of credibility-enhancing displays and have substantial implications for practitioners and policy-makers.

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.

Elite party cues increase vaccination intentions among Republicans.

Overcoming the COVID-19 pandemic requires motivating the vast majority of Americans to get vaccinated. However, vaccination rates have become politically polarized, and a substantial proportion of Republicans have remained vaccine hesitant for months. Here, we explore how endorsements by party elites affect Republicans' COVID-19 vaccination intentions and attitudes. In a preregistered survey experiment (n = 1,480), we varied whether self-identified Republicans saw endorsements of the vaccine from prominent Republicans (including video of a speech by former President Donald Trump), from the Democratic Party (including video of a speech by President Joseph Biden), or a neutral control condition including no endorsements. Unvaccinated Republicans who were exposed to the Republican elite endorsement reported 7.0% higher vaccination intentions than those who viewed the Democratic elite endorsement and 5.7% higher than those in the neutral control condition. These effects were statistically mediated by participants' reports of how much they thought Republican politicians would want them to get vaccinated. We also found evidence of backlash effects against Democratic elites: Republicans who viewed the Democratic elite endorsement reported they would be significantly less likely to encourage others to vaccinate and had more negative attitudes toward the vaccine, compared with those who viewed the Republican elite endorsement or the neutral control. These results demonstrate the relative advantage of cues from Republican elites-and the risks of messaging from Democrats currently in power-for promoting vaccination among the largest vaccine-hesitant subgroup in the United States.

The (minimal) persuasive advantage of political video over text.

Concerns about video-based political persuasion are prevalent in both popular and academic circles, predicated on the assumption that video is more compelling than text. To date, however, this assumption remains largely untested in the political domain. Here, we provide such a test. We begin by drawing a theoretical distinction between two dimensions for which video might be more efficacious than text: 1) one's belief that a depicted event actually occurred and 2) the extent to which one's attitudes and behavior are changed. We test this model across two high-powered survey experiments varying exposure to politically persuasive messaging (total n = 7,609 Americans; 26,584 observations). Respondents were shown a selection of persuasive messages drawn from a diverse sample of 72 clips. For each message, they were randomly assigned to one of three conditions: a short video, a detailed transcript of the video, or a control condition. Overall, we find that individuals are more likely to believe an event occurred when it is presented in video versus textual form, but the impact on attitudes and behavioral intentions is much smaller. Importantly, for both dimensions, these effects are highly stable across messages and respondent subgroups. Moreover, when it comes to attitudes and engagement, the difference between the video and text conditions is comparable to, if not smaller than, the difference between the text and control conditions. Taken together, these results call into question widely held assumptions about the unique persuasive power of political video over text.

Timing matters when correcting fake news.

Countering misinformation can reduce belief in the moment, but corrective messages quickly fade from memory. We tested whether the longer-term impact of fact-checks depends on when people receive them. In two experiments (total N = 2,683), participants read true and false headlines taken from social media. In the treatment conditions, "true" and "false" tags appeared before, during, or after participants read each headline. Participants in a control condition received no information about veracity. One week later, participants in all conditions rated the same headlines' accuracy. Providing fact-checks after headlines (debunking) improved subsequent truth discernment more than providing the same information during (labeling) or before (prebunking) exposure. This finding informs the cognitive science of belief revision and has practical implications for social media platform designers.

Human Cooperation and the Crises of Climate Change, COVID-19, and Misinformation.

Contemporary society is facing many social dilemmas-including climate change, COVID-19, and misinformation-characterized by a conflict between short-term self-interest and longer-term collective interest. The climate crisis requires paying costs today to reduce climate-related harms and risks that we face in the future. The COVID-19 crisis requires the less vulnerable to pay costs to benefit the more vulnerable in the face of great uncertainty. The misinformation crisis requires investing effort to assess truth and abstain from spreading attractive falsehoods. Addressing these crises requires an understanding of human cooperation. To that end, we present (a) an overview of mechanisms for the evolution of cooperation, including mechanisms based on similarity and interaction; (b) a discussion of how reputation can incentivize cooperation via conditional cooperation and signaling; and (c) a review of social preferences that undergird the proximate psychology of cooperation, including positive regard for others, parochialism, and egalitarianism. We discuss the three focal crises facing our society through the lens of cooperation, emphasizing how cooperation research can inform our efforts to address them.

Third-party punishment as a costly signal of trustworthiness.

Third-party punishment (TPP), in which unaffected observers punish selfishness, promotes cooperation by deterring defection. But why should individuals choose to bear the costs of punishing? We present a game theoretic model of TPP as a costly signal of trustworthiness. Our model is based on individual differences in the costs and/or benefits of being trustworthy. We argue that individuals for whom trustworthiness is payoff-maximizing will find TPP to be less net costly (for example, because mechanisms that incentivize some individuals to be trustworthy also create benefits for deterring selfishness via TPP). We show that because of this relationship, it can be advantageous for individuals to punish selfishness in order to signal that they are not selfish themselves. We then empirically validate our model using economic game experiments. We show that TPP is indeed a signal of trustworthiness: third-party punishers are trusted more, and actually behave in a more trustworthy way, than non-punishers. Furthermore, as predicted by our model, introducing a more informative signal--the opportunity to help directly--attenuates these signalling effects. When potential punishers have the chance to help, they are less likely to punish, and punishment is perceived as, and actually is, a weaker signal of trustworthiness. Costly helping, in contrast, is a strong and highly used signal even when TPP is also possible. Together, our model and experiments provide a formal reputational account of TPP, and demonstrate how the costs of punishing may be recouped by the long-run benefits of signalling one's trustworthiness.

Fighting misinformation on social media using crowdsourced judgments of news source quality.

Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content ("fake news"). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans-mostly due to distrust of mainstream sources by Republicans-every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.

A field experiment on community policing and police legitimacy.

Despite decades of declining crime rates, longstanding tensions between police and the public continue to frustrate the formation of cooperative relationships necessary for the function of the police and the provision of public safety. In response, policy makers continue to promote community-oriented policing (COP) and its emphasis on positive, nonenforcement contact with the public as an effective strategy for enhancing public trust and police legitimacy. Prior research designs, however, have not leveraged the random assignment of police-public contact to identify the causal effect of such interactions on individual-level attitudes toward the police. Therefore, the question remains: Do positive, nonenforcement interactions with uniformed patrol officers actually cause meaningful improvements in attitudes toward the police? Here, we report on a randomized field experiment conducted in New Haven, CT, that sheds light on this question and identifies the individual-level consequences of positive, nonenforcement contact between police and the public. Findings indicate that a single instance of positive contact with a uniformed police officer can substantially improve public attitudes toward police, including legitimacy and willingness to cooperate. These effects persisted for up to 21 d and were not limited to individuals inclined to trust and cooperate with the police prior to the intervention. This study demonstrates that positive nonenforcement contact can improve public attitudes toward police and suggests that police departments would benefit from an increased focus on strategies that promote positive police-public interactions.