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Individuals preferentially reciprocate connections with copartisans versus counter-partisans online. However, the mechanisms underlying this partisan bias remain unclear. Do individuals simply prefer viewing politically congenial content, or do they additionally prefer socially connecting with copartisans? Is this driven by preference for in-party ties or distaste for out-party ties? In a Twitter (now called X) field experiment, we created bot accounts varying by partisanship and whether they identified as bots or humans. We randomly assigned Twitter users ( N = 3,013) to be followed by one of these accounts. We found evidence for social motivation-users were much more likely to reciprocate links to copartisan relative to counter-partisan accounts when the accounts identified as humans versus bots. We also found evidence for both in-party preference and out-party dispreference-users were as likely to follow back copartisan accounts as they were unlikely to followback counter-partisan accounts, compared to politically neutral accounts. A follow-up survey experiment ( N = 990) provides further evidence for distinct roles of issue polarization, out-party animosity, and in-party affinity in moderating follow-back decisions online. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Misinformation is a major focus of intervention efforts. Psychological inoculation-an intervention intended to help people identify manipulation techniques-is being adopted at scale around the globe. Yet the efficacy of this approach for increasing belief accuracy remains unclear, as prior work uses synthetic materials that do not contain claims of truth. To address this issue, we conducted five studies with 7,286 online participants using a set of news headlines based on real-world true/false content in which we systematically varied the presence or absence of emotional manipulation. Although an emotional manipulation inoculation did help participants identify emotional manipulation, there was no improvement in participants' ability to tell truth from falsehood. However, when the inoculation was paired with an intervention that draws people's attention to accuracy, the combined intervention did successfully improve truth discernment (by increasing belief in true content). These results provide evidence for synergy between popular misinformation interventions., Competing Interests: Competing interests: G.P. and D.G.R. have received funding from Meta and Google; G.P., A.J.B. and R.C. were employed at Google when the studies were designed and the data were collected; and B.G. both was and is employed at Google. Google has invested in both inoculation and accuracy prompts as misinformation interventions (in addition to supplying funding for these studies). The other authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Content moderators review problematic content for technology companies. One concern is that repeated exposure to false claims could cause moderators to come to believe the very claims they are supposed to moderate, via the "illusory truth effect." In a first lab-in-field experiment ( N = 199) with a global content moderation company, we found that exposure to false claims while working as moderators increased subsequent belief among (mostly Indian and Philippine) employees by 7.1%. We tested an intervention to mitigate this effect: inducing an accuracy mindset. In both general population samples ( N India = 997; N Philippines = 1,184) and a second lab-in-field experiment with professional moderators ( N = 239), inducing participants to consider accuracy when first exposed to the claims eliminates the negative effects of exposure on belief in falsehoods. Our results show that the illusory truth effect and the protective power of an accuracy mindset generalize to non-Western populations and professional moderators., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Scholars warn that partisan divisions in the mass public threaten the health of American democracy. We conducted a megastudy ( n = 32,059 participants) testing 25 treatments designed by academics and practitioners to reduce Americans' partisan animosity and antidemocratic attitudes. We find that many treatments reduced partisan animosity, most strongly by highlighting relatable sympathetic individuals with different political beliefs or by emphasizing common identities shared by rival partisans. We also identify several treatments that reduced support for undemocratic practices-most strongly by correcting misperceptions of rival partisans' views or highlighting the threat of democratic collapse-which shows that antidemocratic attitudes are not intractable. Taken together, the study's findings identify promising general strategies for reducing partisan division and improving democratic attitudes, shedding theoretical light on challenges facing American democracy.

Polarization poses a critical threat to the stability of nations around the world, as it impacts climate change, populism, democracy, and global health. This perspective examines the conceptual understanding, measurement challenges, and potential interventions for polarization. Our analysis highlights the distinction and interactions between the individual and collective levels of polarization, conceptually, methodologically, and in terms of interventions. We conclude by pointing out future directions for understanding polarization and highlighting the interrelations between polarization and other social phenomena., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Descriptive social norms interventions, where a behavior is promoted by learning that others engage in that behavior, are a cornerstone of behavior change research and practice. Here, we examine the effect of learning about the behavior of outgroup members in a hyper-polarized context: mask-wearing during the COVID-19 pandemic. Contrary to prior findings, we find a descriptive social norm "backfire": Across three experiments, Biden supporters increased their mask-wearing intentions after being informed that most Trump supporters never wore masks. We also provide evidence consistent with a mechanism whereby this effect was driven by changes in perceptions about how negatively ingroup members view nonmask wearing. Finally, in a fourth study, Biden supporters show the traditional descriptive norms effect (rather than a backfire) from the same treatment when in a nonpolarized context: dishonesty in a coin-flipping task. These findings help to clarify why descriptive social norm interventions promote behavior change, and underscore the importance of social norms in motivating prosocial behaviors. They also suggest an update to current best practices in the design of descriptive norm interventions: in polarized contexts, it can be beneficial to publicize antisocial behavior of outgroup members., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

In response to intense pressure, technology companies have enacted policies to combat misinformation 1-4 . The enforcement of these policies has, however, led to technology companies being regularly accused of political bias 5-7 . We argue that differential sharing of misinformation by people identifying with different political groups 8-15 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., (© 2024. The Author(s).)

Warning labels from professional fact-checkers are one of the most widely used interventions against online misinformation. But are fact-checker warning labels effective for those who distrust fact-checkers? Here, in a first correlational study (N = 1,000), we validate a measure of trust in fact-checkers. Next, we conduct meta-analyses across 21 experiments (total N = 14,133) in which participants evaluated true and false news posts and were randomized to either see no warning labels or to see warning labels on a high proportion of the false posts. Warning labels were on average effective at reducing belief in (27.6% reduction), and sharing of (24.7% reduction), false headlines. While warning effects were smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in (12.9% reduction), and sharing of (16.7% reduction), false news even for those most distrusting of fact-checkers. These results suggest that fact-checker warning labels are a broadly effective tool for combatting misinformation., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Conspiracy theory beliefs are notoriously persistent. Influential hypotheses propose that they fulfill important psychological needs, thus resisting counterevidence. Yet previous failures in correcting conspiracy beliefs may be due to counterevidence being insufficiently compelling and tailored. To evaluate this possibility, we leveraged developments in generative artificial intelligence and engaged 2190 conspiracy believers in personalized evidence-based dialogues with GPT-4 Turbo. The intervention reduced conspiracy belief by ~20%. The effect remained 2 months later, generalized across a wide range of conspiracy theories, and occurred even among participants with deeply entrenched beliefs. Although the dialogues focused on a single conspiracy, they nonetheless diminished belief in unrelated conspiracies and shifted conspiracy-related behavioral intentions. These findings suggest that many conspiracy theory believers can revise their views if presented with sufficiently compelling evidence.

A frequently invoked explanation for the sharing of false over true political information is that partisans are motivated by their reputations. In particular, it is often argued that by indiscriminately sharing news that is favorable to one's political party, regardless of whether it is true-or perhaps especially when it is not true-partisans can signal loyalty to their group, and improve their reputations in the eyes of their online networks. Across three survey studies (total N = 3,038), and an analysis of over 26,000 tweets, we explored these hypotheses by measuring the reputational benefits that people anticipate and receive from sharing different content online. In the survey studies, we showed participants actual news headlines that varied in (ⅰ) veracity, and (ⅱ) favorability to their preferred political party. Across all three studies, participants anticipated that sharing true news would bring more reputational benefits than sharing false news. Critically, while participants also expected greater reputational benefits for sharing news favorable to their party, the perceived reputation value of veracity was no smaller for more favorable headlines. We found a similar pattern when analyzing engagement on Twitter: among headlines that were politically favorable to a user's preferred party, true headlines elicited more approval than false headlines., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

The spread of misinformation through media and social networks threatens many aspects of society, including public health and the state of democracies. One approach to mitigating the effect of misinformation focuses on individual-level interventions, equipping policymakers and the public with essential tools to curb the spread and influence of falsehoods. Here we introduce a toolbox of individual-level interventions for reducing harm from online misinformation. Comprising an up-to-date account of interventions featured in 81 scientific papers from across the globe, the toolbox provides both a conceptual overview of nine main types of interventions, including their target, scope and examples, and a summary of the empirical evidence supporting the interventions, including the methods and experimental paradigms used to test them. The nine types of interventions covered are accuracy prompts, debunking and rebuttals, friction, inoculation, lateral reading and verification strategies, media-literacy tips, social norms, source-credibility labels, and warning and fact-checking labels., (© 2024. Springer Nature Limited.)

Low uptake of the COVID-19 vaccine in the US has been widely attributed to social media misinformation. To evaluate this claim, we introduce a framework combining lab experiments (total N = 18,725), crowdsourcing, and machine learning to estimate the causal effect of 13,206 vaccine-related URLs on the vaccination intentions of US Facebook users ( N ≈ 233 million). We estimate that the impact of unflagged content that nonetheless encouraged vaccine skepticism was 46-fold greater than that of misinformation flagged by fact-checkers. Although misinformation reduced predicted vaccination intentions significantly more than unflagged vaccine content when viewed, Facebook users' exposure to flagged content was limited. In contrast, unflagged stories highlighting rare deaths after vaccination were among Facebook's most-viewed stories. Our work emphasizes the need to scrutinize factually accurate but potentially misleading content in addition to outright falsehoods.

There is strong political assortment of Americans on social media networks. This is typically attributed to preferential tie formation (i.e. homophily) among those with shared partisanship. Here, we demonstrate an additional factor beyond homophily driving assorted networks: preferential prevention of social ties. In two field experiments on Twitter, we created human-looking bot accounts that identified as Democrats or Republicans, and then randomly assigned users to be followed by one of these accounts. In addition to preferentially following-back copartisans, we found that users were 12 times more likely to block counter-partisan accounts compared to copartisan accounts in the first experiment, and 4 times more likely to block counter-partisan accounts relative to a neutral account or a copartisan account in the second experiment. We then replicated these findings in a survey experiment and found evidence of a key motivation for blocking: wanting to avoid seeing any content posted by the blocked user. Additionally, we found that Democrats preferentially blocked counter-partisans more than Republicans, and that this asymmetry was likely due to blocking accounts who post low-quality or politically slanted content (rather than an asymmetry in identity-based blocking). Our results demonstrate that preferential blocking of counter-partisans is an important phenomenon driving political assortment on social media., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

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.

There is considerable concern about users posting misinformation and harmful language on social media. Substantial-yet largely distinct-bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (β = 0.10); and false headlines contain more harmful language than true headlines (β = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language-even in non-news posts that are unrelated to (mis)information (β = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

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., Competing Interests: Competing interests statement:The authors declare no competing interest.

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.

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., Competing Interests: Competing interests statement:The authors declare no competing interest.

Scientific evidence regularly guides policy decisions 1 , with behavioural science increasingly part of this process 2 . In April 2020, an influential paper 3 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., (© 2023. The Author(s).)

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., Competing Interests: Declaration of competing interest Other research by D.G.R. is funded by gifts from Google and Meta., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Recent work suggests that personality moderates the relationship between political ideology and the sharing of misinformation. Specifically, Lawson and Kakkar (2022) claimed that fake news sharing was driven mostly by low conscientiousness conservatives. We reanalyzed their data and conducted five new preregistered conceptual replications to reexamine their claims ( N = 2,433; stopping rule determined via Bayesian sequential sampling). The results did not support their claim that conscientious conservatives shared less fake news; instead, their findings pertain to overall sharing rates (of both true and fake news), rather than specifically to fake news. That is, the association between conscientiousness and misinformation sharing (when it occurs) is explained by lower overall sharing instead of a particular resistance to fake news per se. Our results highlight the importance of distinguishing between overall sharing tendencies and the sharing of misinformation specifically, which have different theoretical and practical implications for how to combat the spread of misinformation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The spread of misinformation online is a global problem that requires global solutions. To that end, we conducted an experiment in 16 countries across 6 continents (N = 34,286; 676,605 observations) to investigate predictors of susceptibility to misinformation about COVID-19, and interventions to combat the spread of this misinformation. In every country, participants with a more analytic cognitive style and stronger accuracy-related motivations were better at discerning truth from falsehood; valuing democracy was also associated with greater truth discernment, whereas endorsement of individual responsibility over government support was negatively associated with truth discernment in most countries. Subtly prompting people to think about accuracy had a generally positive effect on the veracity of news that people were willing to share across countries, as did minimal digital literacy tips. Finally, aggregating the ratings of our non-expert participants was able to differentiate true from false headlines with high accuracy in all countries via the 'wisdom of crowds'. The consistent patterns we observe suggest that the psychological factors underlying the misinformation challenge are similar across different regional settings, and that similar solutions may be broadly effective., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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.

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.

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.

Why is disbelief in anthropogenic climate change common despite broad scientific consensus to the contrary? A widely held explanation involves politically motivated (system 2) reasoning: Rather than helping uncover the truth, people use their reasoning abilities to protect their partisan identities and reject beliefs that threaten those identities. Despite the popularity of this account, the evidence supporting it (i) does not account for the fact that partisanship is confounded with prior beliefs about the world and (ii) is entirely correlational with respect to the effect of reasoning. Here, we address these shortcomings by (i) measuring prior beliefs and (ii) experimentally manipulating participants' extent of reasoning using cognitive load and time pressure while they evaluate arguments for or against anthropogenic global warming. The results provide no support for the politically motivated system 2 reasoning account over other accounts: Engaging in more reasoning led people to have greater coherence between judgments and their prior beliefs about climate change-a process that can be consistent with rational (unbiased) Bayesian reasoning-and did not exacerbate the impact of partisanship once prior beliefs are accounted for., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

It is widely assumed that party identification and loyalty can distort partisans' information processing, diminishing their receptivity to counter-partisan arguments and evidence. Here we empirically evaluate this assumption. We test whether American partisans' receptivity to arguments and evidence is diminished by countervailing cues from in-party leaders (Donald Trump or Joe Biden), using a survey experiment with 24 contemporary policy issues and 48 persuasive messages containing arguments and evidence (N = 4,531; 22,499 observations). We find that, while in-party leader cues influenced partisans' attitudes, often more strongly than the persuasive messages, there was no evidence that the cues meaningfully diminished partisans' receptivity to the messages-despite them directly contradicting the messages. Rather, persuasive messages and countervailing leader cues were integrated as independent pieces of information. These results generalized across policy issues, demographic subgroups and cue environments, and challenge existing assumptions about the extent to which party identification and loyalty distort partisans' information processing., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Acts of extraordinary, costly altruism, in which significant risks or costs are assumed to benefit strangers, have long represented a motivational puzzle. But the features that consistently distinguish individuals who engage in such acts have not been identified. We assess six groups of real-world extraordinary altruists who had performed costly or risky and normatively rare (<0.00005% per capita) altruistic acts: heroic rescues, non-directed and directed kidney donations, liver donations, marrow or hematopoietic stem cell donations, and humanitarian aid work. Here, we show that the features that best distinguish altruists from controls are traits and decision-making patterns indicating unusually high valuation of others' outcomes: high Honesty-Humility, reduced Social Discounting, and reduced Personal Distress. Two independent samples of adults who were asked what traits would characterize altruists failed to predict this pattern. These findings suggest that theories regarding self-focused motivations for altruism (e.g., self-enhancing reciprocity, reputation enhancement) alone are insufficient explanations for acts of real-world self-sacrifice., (© 2023. The Author(s).)

There is widespread concern that rising affective polarization-particularly dislike for outpartisans-exacerbates Americans' anti-democratic attitudes. Accordingly, scholars and practitioners alike have invested great effort in developing depolarization interventions that reduce affective polarization. Critically, however, it remains unclear whether these interventions reduce anti-democratic attitudes, or only change sentiments towards outpartisans. Here we address this question with experimental tests (total n = 8,385) of three previously established depolarization interventions: correcting misperceptions of outpartisans, priming inter-partisan friendships and observing warm cross-partisan interactions between political leaders. While these depolarization interventions reliably reduced affective polarization, we do not find compelling evidence that these interventions reduced support for undemocratic candidates, support for partisan violence or prioritizing partisan ends over democratic means. Thus, future efforts to strengthen pro-democratic attitudes may do better if they target these outcomes directly. More broadly, these findings call into question the previously assumed causal effect of affective polarization on anti-democratic attitudes., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Recent experiments have found that prompting people to think about accuracy reduces misinformation sharing intentions. The process by which this effect operates, however, remains unclear. Do accuracy prompts cause people to "stop and think," increasing deliberation? Or do they change what people think about, drawing attention to accuracy? Since these two accounts predict the same behavioral outcomes (i.e., increased sharing discernment following a prompt), we used computational modeling of sharing decisions with response time data, as well as out-of-sample ratings of headline perceived accuracy, to test the accounts' divergent predictions across six studies (N = 5633). The results suggest that accuracy prompts do not increase the amount of deliberation people engage in. Instead, they increase the weight participants put on accuracy while deliberating. By showing that prompting people makes them think better even without thinking more, our results challenge common dual-process interpretations of the accuracy-prompt effect. Our findings also highlight the importance of understanding how social media distracts people from considering accuracy, and provide evidence for scalable interventions that redirect people's attention., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Misinformation can come directly from public figures and organizations (referred to here as "elites"). Here, we develop a tool for measuring Twitter users' exposure to misinformation from elites based on the public figures and organizations they choose to follow. Using a database of professional fact-checks by PolitiFact, we calculate falsity scores for 816 elites based on the veracity of their statements. We then assign users an elite misinformation-exposure score based on the falsity scores of the elites they follow on Twitter. Users' misinformation-exposure scores are negatively correlated with the quality of news they share themselves, and positively correlated with estimated conservative ideology. Additionally, we analyze the co-follower, co-share, and co-retweet networks of 5000 Twitter users and find an ideological asymmetry: estimated ideological extremity is associated with more misinformation exposure for users estimated to be conservative but not for users estimated to be liberal. Finally, we create an open-source R library and an Application Programming Interface (API) making our elite misinformation-exposure estimation tool openly available to the community., (© 2022. The Author(s).)

Misinformation is a serious concern for societies across the globe. To design effective interventions to combat the belief in and spread of misinformation, we must understand which psychological processes influence susceptibility to misinformation. This paper tests the widely assumed - but largely untested - claim that emotionally provocative headlines are associated with worse ability to identify true versus false headlines. Consistent with this proposal, we found correlational evidence that overall emotional response at the headline level is associated with diminished truth discernment, except for experienced anger which was associated with increased truth discernment. The second set of studies tested a popular emotion regulation intervention where people were asked to apply either emotional suppression or emotion reappraisal techniques when considering the veracity of several headlines. In contrast to the correlation results, we found no evidence that emotion regulation helped people distinguish false from true news headlines.

What are the psychological consequences of the increasingly politicized nature of the COVID-19 pandemic in the United States relative to similar Western countries? In a two-wave study completed early (March) and later (December) in the pandemic, we found that polarization was greater in the United States ( N = 1,339) than in Canada ( N = 644) and the United Kingdom. ( N = 1,283). Political conservatism in the United States was strongly associated with engaging in weaker mitigation behaviors, lower COVID-19 risk perceptions, greater misperceptions, and stronger vaccination hesitancy. Although there was some evidence that cognitive sophistication was associated with increased polarization in the United States in December (but not March), cognitive sophistication was nonetheless consistently negatively correlated with misperceptions and vaccination hesitancy across time, countries, and party lines. Furthermore, COVID-19 skepticism in the United States was strongly correlated with distrust in liberal-leaning mainstream news outlets and trust in conservative-leaning news outlets, suggesting that polarization may be driven by differences in information environments.

Interventions that shift users attention toward the concept of accuracy represent a promising approach for reducing misinformation sharing online. We assess the replicability and generalizability of this accuracy prompt effect by meta-analyzing 20 experiments (with a total N = 26,863) completed by our group between 2017 and 2020. This internal meta-analysis includes all relevant studies regardless of outcome and uses identical analyses across all studies. Overall, accuracy prompts increased the quality of news that people share (sharing discernment) relative to control, primarily by reducing sharing intentions for false headlines by 10% relative to control in these studies. The magnitude of the effect did not significantly differ by content of headlines (politics compared with COVID-19 related news) and did not significantly decay over successive trials. The effect was not robustly moderated by gender, race, political ideology, education, or value explicitly placed on accuracy, but was significantly larger for older, more reflective, and more attentive participants. This internal meta-analysis demonstrates the replicability and generalizability of the accuracy prompt effect on sharing discernment., (© 2022. The Author(s).)

A meaningful portion of online misinformation sharing is likely attributable to Internet users failing to consider accuracy when deciding what to share. As a result, simply redirecting attention to the concept of accuracy can increase sharing discernment. Here we discuss the importance of accuracy and describe a limited-attention utility model that is based on a theory about inattention to accuracy on social media. We review research that shows how a simple nudge or prompt that shifts attention to accuracy increases the quality of news that people share (typically by decreasing the sharing of false content), and then discuss outstanding questions relating to accuracy nudges, including the need for more work relating to persistence and habituation as well as the dearth of cross-cultural research on these topics. We also make several recommendations for policy-makers and social media companies for how to implement accuracy nudges., (© 2022 by The American Academy of Political and Social Science.)

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., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

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.

On March 16, 2020, the US Government introduced strict social distancing protocols for the United States in an effort to stem the spread of the COVID-19 pandemic. This had an immediate major effect on the job market, with millions of Americans forced to find alternative ways to make a living from home. As online labor markets like Amazon Mechanical Turk (MTurk) play a major role in social science research, concerns have been raised that the pandemic may be reducing the diversity of subjects participating in experiments. Here, we investigate this possibility empirically. Specifically, we look at 15,539 responses gathered in 23 studies run on MTurk between February and July 2020, examining the distribution of gender, age, ethnicity, political preference, and analytic cognitive style. We find notable changes on some of the measures following the imposition of nationwide social distancing: participants are more likely to be less reflective (as measured by the Cognitive Reflection Test), and somewhat less likely to be white, Democrats (traditionally over-represented on MTurk), and experienced with MTurk. Most of these differences are explained by an influx of new participants into the MTurk subject pool who are more diverse and representative - but also less attentive - than previous MTurkers., (© 2021. The Psychonomic Society, Inc.)