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

Election Disinformation in Different Languages is a Big Problem in the U.S.

And it’s driving a wedge between voters in non-English communities Mis- and disinformation about elections predate the endless scroll of modern social media services. [1] Yet easy access to online information channels and amplification tools enable false narratives to spread at a massive scale. When false narratives are combined with data voids and unique cultural […]

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.

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.

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).

Redesigning algorithms to intervene on social norm misperceptions during a national election

For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.

Recent Utah, Texas incidents fuel rumors and political narratives undermining trust in election integrity

A CIP Election Rumor Rundown

Unveiling the waves of mis- and disinformation from social media

In the digital era, social media platforms have become the focal point for public discourse, with a significant impact on shaping societal narratives. However, they are also rife with mis- and disinformation, which can rapidly disseminate and influence public opinion. This paper investigates the propagation of mis- and disinformation on X, a social media platform formerly known as Twitter. We employ a multidimensional analytical approach, integrating sentiment analysis, wavelet analysis, and network analysis to discern the patterns and intensity of misleading information waves. Sentiment analysis elucidates the emotional tone and subjective context within which information is framed. Wavelet analysis reveals the temporal dynamics and persistence of disinformation trends over time. Network analysis maps the intricate web of information flow, identifying key nodes and vectors of virality. The results offer a granular understanding of how false narratives are constructed and sustained within the digital ecosystem. This study contributes to the broader field of digital media literacy by highlighting the urgent need for robust analytical tools to navigate and neutralize the infodemic in the age of social media.

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.

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.

The spreading of misinformation online

Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size. , The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.

The Plot to Subvert an Election: Unraveling the Russia Story So Far (Published 2018)

For two years, Americans have tried to absorb the details of the 2016 attack: spies, leaked emails, social media fraud — and President Trump’s claims that it’s all a hoax. The Times explores what we know and what it means.

Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election

Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of X's algorithmic content recommendations during the 2024 U.S. Presidential Election by deploying 120 sock-puppet monitoring accounts to capture tweets from their personalized "For You" timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning user profiles and assess any potential inequalities and biases in political exposure. Our findings indicate that X's algorithm skews exposure toward a few high-popularity accounts across all users, with right-leaning users experiencing the highest level of exposure inequality. Both left- and right-leaning users encounter amplified exposure to accounts aligned with their own political views and reduced exposure to opposing viewpoints. Additionally, we observe that new accounts experience a right-leaning bias in exposure within their default timelines. Our work contributes to understanding how content recommendation systems may induce and reinforce biases while exacerbating vulnerabilities among politically polarized user groups. We underscore the importance of transparency-aware algorithms in addressing critical issues such as safeguarding election integrity and fostering a more informed digital public sphere.

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.

Stop and Consider:Our campaign

During a federal election there is a large amount of information being distributed, much of it online, which is seeking to influence your vote. Some of this may be disinformation, or false information.

How to break populist parties’ appeal? An experimental evaluation of communication counter-strategies

Right-wing populist parties have become a major challenge to liberal democracies worldwide. How can mainstream parties effectively counter their appeal? While prior research has focused on the drivers of populist support, much less is known about how to counter right-wing populist rhetoric. We test four communication counter-strategies that target core elements of right-wing populist appeals: (i) undermining populists’ claims to democratic credibility, (ii) promoting inclusive in-group identity to counter exclusionary narratives, (iii) highlighting their performance legitimacy, and (iv) emphasizing the procedural legitimacy of mainstream parties. Drawing on real-world party communications from social media, manifestos, and press releases, we created 170 experimental treatments (posters and social media posts varying in format and complexity) to simulate how voters encounter political messages in everyday life. In a large-scale survey experiment with over 24,000 respondents in Germany, messages undermining right-wing populists’ democratic credibility prove most effective, particularly among citizens with high initial support for such parties.