When Corrections Fail: The Persistence of Political Misperceptions

Facts and Myths about Misperceptions

Misperceptions threaten to warp mass opinion and public policy on controversial issues in politics, science, and health. What explains the prevalence and persistence of these false and unsupported beliefs, which seem to be genuinely held by many people? Though limits on cognitive resources and attention play an important role, many of the most destructive misperceptions arise in domains where individuals have weak incentives to hold accurate beliefs and strong directional motivations to endorse beliefs that are consistent with a group identity such as partisanship. These tendencies are often exploited by elites who frequently create and amplify misperceptions to influence elections and public policy. Though evidence is lacking for claims of a “post-truth” era, changes in the speed with which false information travels and the extent to which it can find receptive audiences require new approaches to counter misinformation. Reducing the propagation and influence of false claims will require further efforts to inoculate people in advance of exposure (for example, media literacy), debunk false claims that are already salient or widespread (for example, fact-checking), reduce the prevalence of low-quality information (for example, changing social media algorithms), and discourage elites from promoting false information (for example, strengthening reputational sanctions).

The Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence

Can citizens heed factual information, even when such information challenges their partisan and ideological attachments? The “backfire effect,” described by Nyhan and Reifler (Polit Behav 32(2):303–330. https://doi.org/10.1007/s11109-010-9112-2, 2010), says no: rather than simply ignoring factual information, presenting respondents with facts can compound their ignorance. In their study, conservatives presented with factual information about the absence of Weapons of Mass Destruction in Iraq became more convinced that such weapons had been found. The present paper presents results from five experiments in which we enrolled more than 10,100 subjects and tested 52 issues of potential backfire. Across all experiments, we found no corrections capable of triggering backfire, despite testing precisely the kinds of polarized issues where backfire should be expected. Evidence of factual backfire is far more tenuous than prior research suggests. By and large, citizens heed factual information, even when such information challenges their ideological commitments.

The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?

This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf"' over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.

A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

A meta-analysis was conducted to examine the extent of continued influence of misinformation in the face of correction and the theoretical explanations of this phenomenon. Aggregation of results from 32 studies ( N = 6,527) revealed that, on average, correction does not entirely eliminate the effect of misinformation ( r = –.05, p = .045). Corrective messages were found to be more successful when they are coherent, consistent with the audience’s worldview, and delivered by the source of the misinformation itself. Corrections are less effective if the misinformation was attributed to a credible source, the misinformation has been repeated multiple times prior to correction, or when there was a time lag between the delivery of the misinformation and the correction. These findings are consistent with predictions based on theories of mental models and offer concrete recommendations for practitioners.

Capturing our Attention

What are the harms of misinformation? It’s widely believed that it causes false belief, including false beliefs in quite bizarre claims. I argue that this threat is greatly exaggerated: while plaus...

Dialogues on Democracy: Belief-Tailored AI Conversations Reduce Inaccurate Election Denial Beliefs

Elections, while central to democratic functioning, have become increasingly threatened by beliefs about election fraud. Artificial intelligence (AI) provides a novel opportunity to address such false beliefs through dynamic conversation and debunking. Through two experiments (N = 1,802 Republicans from Lucid who endorsed election fraud claims), we examined the use of AI to fact-check 2020 election conspiracies prior to the 2024 US Presidential election. We tested the effects of two treatments (an information-tailored and values-tailored AI dialogue) in which AI fact-checked their claims and tailored arguments to the specific election conspiracy that the participant themselves articulated. Both treatment conditions, when compared to a control dialogue and a simple statement that the conspiracy was incorrect, reduced confidence in their election conspiracy claims. There was no significant difference between information-tailored and values-tailored feedback. Promisingly, participants with the strongest baseline denialism experienced the largest decreases in denialism beliefs. These studies highlight the potential of AI-driven interventions to address election misinformation.

Anticipating Election Misinformation in the 2026 Midterms

Election misinformation often follows the same pattern every cycle. Here are some of the false and misleading claims that voters are likely to see.

When Do Parties Lie? Misinformation and Radical-Right Populism Across 26 Countries

The spread of misinformation has emerged as a global concern. Academic attention has recently shifted to emphasize the role of political elites as drivers of misinformation. Yet, little is known of the relationship between party politics and the spread of misinformation—in part due to a dearth of cross-national empirical data needed for comparative study. This article examines which parties are more likely to spread misinformation, by drawing on a comprehensive database of 32M tweets from parliamentarians in 26 countries, spanning 6 years and several election periods. The dataset is combined with external databases such as Parlgov and V-Dem, linking the spread of misinformation to detailed information about political parties and cabinets, thus enabling a comparative politics approach to misinformation. Using multilevel analysis with random country intercepts, we find that radical-right populism is the strongest determinant for the propensity to spread misinformation. Populism, left-wing populism, and right-wing politics are not linked to the spread of misinformation. These results suggest that political misinformation should be understood as part and parcel of the current wave of radical right populism, and its opposition to liberal democratic institution.

Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation

This meta-analysis investigated the factors underlying effective messages to counter attitudes and beliefs based on misinformation. Because misinformation can lead to poor decisions about consequential matters and is persistent and difficult to correct, debunking it is an important scientific and public-policy goal. This meta-analysis ( k = 52, N = 6,878) revealed large effects for presenting misinformation ( ds = 2.41–3.08), debunking ( ds = 1.14–1.33), and the persistence of misinformation in the face of debunking ( ds = 0.75–1.06). Persistence was stronger and the debunking effect was weaker when audiences generated reasons in support of the initial misinformation. A detailed debunking message correlated positively with the debunking effect. Surprisingly, however, a detailed debunking message also correlated positively with the misinformation-persistence effect.

Explicit warnings reduce but do not eliminate the continued influence of misinformation

Information that initially is presumed to be correct, but that is later retracted or corrected, often continues to influence memory and reasoning. This occurs even if the retraction itself is well remembered. The present study investigated whether the continued influence of misinformation can be reduced by explicitly warning people at the outset that they may be misled. A specific warning— giving detailed information about the continued influence effect (CIE)—succeeded in reducing the continued reliance on outdated information but did not eliminate it. A more general warning—reminding people that facts are not always properly checked before information is disseminated—was even less effective. In an additional experiment, a specific warning was combined with the provision of a plausible alternative explanation for the retracted information. This combined manipulation further reduced the CIE but still failed to eliminate it altogether.

The Role of Media in Political Polarization| Inoculation Can Reduce the Perceived Reliability of Polarizing Social Media Content

Little research is available on psychological interventions that counter susceptibility to polarizing online content. We conducted 3 studies (n1 = 472, n2 = 193, n3 = 772) to evaluate whether psychological resistance against polarizing social media content can be conferred, using the Bad News game, a “technique-based inoculation” intervention that simulates a social media feed. We investigate (1) whether technique-based inoculation can reduce susceptibility to content designed to fuel intergroup polarization; (2) whether technique-based inoculation can offer cross-protection against misinformation techniques that people were not inoculated against; and (3) whether political ideology plays a role in how people engage with anti-misinformation interventions. In Studies 1 and 3 (but not Study 2), we found that technique-based inoculation significantly reduces the perceived reliability of polarizing content and offers partial cross-protection against untreated misinformation techniques. We found no effect for attitudinal certainty and news-sharing intentions. Finally, we report preliminary evidence that people may choose to engage with politically congruent news topics within the intervention.

Misplaced Divides? Discussing Political Disagreement With Strangers Can Be Unexpectedly Positive

Differences of opinion between people are common in everyday life, but discussing those differences openly in conversation may be unnecessarily rare. We report three experiments ( N = 1,264 U.S.-based adults) demonstrating that people’s interest in discussing important but potentially divisive topics is guided by their expectations about how positively the conversation will unfold, leaving them more interested in having a conversation with someone who agrees versus disagrees with them. People’s expectations about their conversations, however, were systematically miscalibrated such that people underestimated how positive these conversations would be—especially in cases of disagreement. Miscalibrated expectations stemmed from underestimating the degree of common ground that would emerge in conversation and from failing to appreciate the power of social forces in conversation that create social connection. Misunderstanding the outcomes of conversation could lead people to avoid discussing disagreements more often, creating a misplaced barrier to learning, social connection, free inquiry, and free expression.

The Twilight of the Polls? A Review of Trends in Polling Accuracy and the Causes of Polling Misses

Polls have had a number of high-profile misses in recent elections. We review the current polling environment, the performance of polls in a historical context, the mechanisms of polling error, and the causes of several recent misses in Britain and the US. Contrary to conventional wisdom, polling errors have been constant over time, although the level of error has always been substantially beyond that implied by stated margins of error. Generally, there is little evidence that voters lying about their vote intention (so-called ‘shy’ voters) is a substantial cause of polling error. Instead, polling errors have most commonly resulted from problems with representative samples and weighting, undecided voters breaking in one direction, and to a lesser extent late swings and turnout models. We conclude with a discussion of future directions for polling both in terms of fixing the problems identified and new approaches to understanding public opinion.

Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor

Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier models on the political left. A parallel literature shows that LLMs are sycophantic: they adapt their answers to the views, identities, and expectations of the user. We show that these findings are linked: standard political-bias audits partly capture sycophantic accommodation to the inferred auditor. We employ a factorial experiment across three major audit instruments--the Political Compass Test, the Pew Political Typology, and 1,540 partisan-benchmarked Pew American Trends Panel items--administered to six frontier LLMs while varying only the asker's stated identity (N = 30,990 responses). At baseline, all six models lean left. When the asker identifies as a conservative Republican, responses shift sharply: the share of items closer to Democrats falls by 28-62 percentage points, and all six models move right of center. A mirror-image progressive-Democrat cue produces little change; rightward accommodation is 8.0$\times$ larger than leftward. When asked who the default asker is, models identify an auditor, researcher, or academic; when asked what answer that asker expects, they select the Democrat-coded option 75% of the time, nearly the rate under an explicit progressive cue. These patterns are inconsistent with a purely fixed model ideology and indicate that single-prompt audits capture an interaction between model and inferred interlocutor. Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile that must be mapped across realistic interlocutors.

Deep Storytelling: Collective Sensemaking and Layers of Meaning in U.S. Elections

Misinformation and disinformation about elections remain pressing concerns for researchers, policymakers, and the public. Critics, however, argue that fears surrounding these issues are exaggerated due to a lack of evidence of impact. This debate highlights the challenges inherent in assessing the impacts of misinformation, as the drivers of false and misleading content often exist in the context of a specific claim. To address this issue, we examined false and misleading information surrounding the 2020 and 2022 U.S. national elections, focusing on the contextual features of online conversations that fueled various rumors. We developed two qualitative codebooks, creating the second after realizing that the first, which labeled individual tweets, failed to capture broader rumoring dynamics. By integrating multi-layered qualitative coding with thematic analysis and quantitative visualizations, we show how influencers, political elites, and audiences collaboratively told deep stories from 2020 through 2022. As these stories were told, audiences interpreted events in 2022 through the lens of the 2020 story, guided by influencers' cues, leading to an evolution in storytelling style between the two election cycles. This ongoing performance was tailored to align with the incentive structures, affordances, and attention economy of social media. We combine deep stories with theories of collective sensemaking and rumoring, creating a framework to better assess the contextual features surrounding false and misleading information.

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any… | Ioana A. Cristea

So maybe now we can agree that having an unchecked research integrity militia who unfortunately has the ear of the press and increasingly that of the the publishers, but who fiercely rejects any minimal ethics or code of conduct, is a (growing) problem? Also, that 1. problems have to be investigated before deciding they are are legitimate and serious; 2. this investigation is not social media, blogs and the press; 3. this investigation should not be on the front page of journals and Retraction Watch; 4. there are degrees of seriousness and some things can just be corrected or are simply not very consequential (no, it's not a house of bricks where we have to check every brick, that's a dumb analogy), so being absolutely hysterical and overdramatic about any lie, inaccuracy or mistake is purposeful at worse and should be ignored at best and 5. it is not only unnecessary, but harmful, to also go into other, non-academic things the person did or does to complete "investigations" that you (press, sleuth, blogger, etc) do not have the tools and information to do completely and accurately (this fixation would be called harassment in the before times). Others will justly write about what the institutions did or did not do, but what I want to say is that we really should end it with the blank credit we give to any and all allegations that come from the establish research integrity truth fighters.