Toolbox of individual-level interventions against online misinformation.

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.

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.

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.

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.

High level of correspondence across different news domain quality rating sets.

One widely used approach for quantifying misinformation consumption and sharing is to evaluate the quality of the news domains that a user interacts with. However, different media organizations and fact-checkers have produced different sets of news domain quality ratings, raising questions about the reliability of these ratings. In this study, we compared six sets of expert ratings and found that they generally correlated highly with one another. We then created a comprehensive set of domain ratings for use by the research community (github.com/hauselin/domain-quality-ratings), leveraging an ensemble "wisdom of experts" approach. To do so, we performed imputation together with principal component analysis to generate a set of aggregate ratings. The resulting rating set comprises 11,520 domains-the most extensive coverage to date-and correlates well with other rating sets that have more limited coverage. Together, these results suggest that experts generally agree on the relative quality of news domains, and the aggregate ratings that we generate offer a powerful research tool for evaluating the quality of news consumed or shared and the efficacy of misinformation interventions.

Conscientiousness does not moderate the association between political ideology and susceptibility to fake news sharing.

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) 2023 APA, all rights reserved).

Deliberation is (probably) triggered and sustained by multiple mechanisms.

De Neys proposes that deliberation is triggered and sustained by uncertainty. I argue that there are cases where deliberation occurs with low uncertainty - such as when problems are excessively complicated and the reasoner decides against engaging in deliberation - and that there are likely multiple factors that lead to (or undermine) deliberation. Nonetheless, De Neys is correct to surface these issues.

Understanding and combatting misinformation across 16 countries on six continents.

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.

The influence of sociodemographic factors on COVID-19 vaccine certificate acceptance: A cross-sectional study.

Vaccine certificates have been implemented worldwide, aiming to promote vaccination rates and to reduce the spread of COVID-19. However, their use during the COVID-19 pandemic was controversial and has been criticized for infringing upon medical autonomy and individual rights. We administered a national online survey exploring social and demographic factors predicting the degree of public approval of vaccine certificates in Canada. We conducted a multivariate linear regression which revealed which factors were predictive of vaccine certificate acceptance in Canada. Self-reported minority status (p < .001), rurality (p < .001), political ideology (p < .001), age (p < .001), having children under 18 in the household (p < .001), education (p = .014), and income status (p = .034) were significant predictors of attitudes toward COVID-19 vaccine certificates. We observed the lowest vaccine-certificate approval among participants who: self-identify as a visible minority; live in rural areas; are politically conservative; are 18-34 years of age; have children under age 18 living in the household; have completed an apprenticeship or trades education; and those with an annual income between $100,000-$159,999. The present findings are valuable for their ability to inform the implementation of vaccine certificates during future pandemic scenarios which may require targeted communication between public health agencies and under-vaccinated populations.

People are worse at detecting fake news in their foreign language.

Across two preregistered within-subject experiments (N = 570), we found that when using their foreign language, proficient bilinguals discerned true from false news less accurately. This was the case for international news (Experiment 1) and more local news (Experiment 2). When using a foreign (as opposed to native) language, false news headlines were always judged more believable, while true news headlines were judged equally (Experiment 2) or less believable (Experiment 1). In contrast to past theorizing, the foreign language effect interacted neither with perceived arousal of news (Experiment 1) nor with individual differences in cognitive reflection (Experiments 1 and 2). Finally, using signal detection theory modeling, we showed that the negative effects of using a foreign language were not caused by adopting different responding strategies (e.g., preferring omissions to false alarms) but rather by decreased sensitivity to the truth. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Reasoning about climate change.

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.

On the Disposition to Think Analytically: Four Distinct Intuitive-Analytic Thinking Styles.

Many measures have been developed to index intuitive versus analytic thinking. Yet it remains an open question whether people primarily vary along a single dimension or if there are genuinely different types of thinking styles. We distinguish between four distinct types of thinking styles: Actively Open-minded Thinking, Close-Minded Thinking, Preference for Intuitive Thinking, and Preference for Effortful Thinking. We discovered strong predictive validity across several outcome measures (e.g., epistemically suspect beliefs, bullshit receptivity, empathy, moral judgments), with some subscales having stronger predictive validity for some outcomes but not others. Furthermore, Actively Open-minded Thinking, in particular, strongly outperformed the Cognitive Reflection Test in predicting misperceptions about COVID-19 and the ability to discern between vaccination-related true and false news. Our results indicate that people do, in fact, differ along multiple dimensions of intuitive-analytic thinking styles and that these dimensions have consequences for understanding a wide range of beliefs and behaviors.

The social media context interferes with truth discernment.

There is widespread concern about misinformation circulating on social media. In particular, many argue that the context of social media itself may make people susceptible to the influence of false claims. Here, we test that claim by asking whether simply considering sharing news on social media reduces the extent to which people discriminate truth from falsehood when judging accuracy. In a large online experiment examining coronavirus disease 2019 (COVID-19) and political news (N = 3157 Americans), we find support for this possibility. When judging the accuracy of headlines, participants were worse at discerning truth from falsehood if they both evaluated accuracy and indicated their sharing intentions, compared to just evaluating accuracy. These results suggest that people may be particularly vulnerable to believing false claims on social media, given that sharing is a core element of what makes social media "social."

Science beliefs, political ideology, and cognitive sophistication.

Some theoretical models assume that a primary source of contention surrounding science belief is political and that partisan disagreement drives beliefs; other models focus on basic science knowledge and cognitive sophistication, arguing that they facilitate proscientific beliefs. To test these competing models, we identified a range of controversial issues subject to potential ideological disagreement and examined the roles of political ideology, science knowledge, and cognitive sophistication on science beliefs. Our results indicate that there was surprisingly little partisan disagreement on a wide range of contentious scientific issues. We also found weak evidence for identity-protective cognition (where cognitive sophistication exacerbates partisan disagreement); instead, cognitive sophistication (i.e., reasoning ability) was generally associated with proscience beliefs. In two studies focusing on anthropogenic climate change, we found that increased political motivations did not increase polarization among individuals who are higher in cognitive sophistication, which indicates that increased political motivations might not have as straightforward an impact on science beliefs as has been assumed in the literature. Finally, our findings indicate that basic science knowledge is the most consistent predictor of people's beliefs about science across a wide range of issues. These results suggest that educators and policymakers should focus on increasing basic science literacy and critical thinking rather than on the ideologies that purportedly divide people. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Thinking more or thinking differently? Using drift-diffusion modeling to illuminate why accuracy prompts decrease misinformation sharing.

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.

Intuition, reason, and conspiracy beliefs.

Conspiracy theories tend to involve doubt and skepticism, but are conspiracy believers really more deliberative? We review recent research that investigates the relative roles of intuition and reason in conspiracy belief and find that the preponderance of evidence indicates that conspiracy belief is linked to an overreliance on intuition and a lack of reflection. This research, in addition to work investigating the broader influence of misinformation, indicates that people may believe conspiracies partly because they fail to engage in analytic thinking and rely too much on their intuitions. However, we also note that research in this area lacks in experimental studies and that work focusing on underlying cognitive mechanisms is needed.

Nudging Social Media toward Accuracy.

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.

Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation.

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.