Keeping democracies and the information they depend on trustworthy.

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, interventions are needed at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

Democracies depend on shared standards of reality. We investigated whether misplaced certainty in political leaders’ rhetoric—the assertion of evidence- and consensus-defying claims as facts—heightens support for political violence. Across five preregistered studies combining historical analyses, large-scale social media data, and experiments, misplaced certainty in elite political rhetoric was associated with subsequent increases in violent political events, predicted greater diffusion and violence-supportive responding online, and experimentally increased support for political violence and its psychological precursors—affective polarization, moral outrage, and perceived threat. These patterns were observed beyond rhetorical certainty and inaccuracy alone, and generalized across diverse political parties, leaders, participant characteristics, and rhetorical features. Together, the findings reveal that the societal consequences of false claims in elite political rhetoric depend not only on their inaccuracy, but on the epistemic stance with which leaders present them.

Nearly five billion people use and receive news through social media and there is widespread concern about the negative consequences of misinformation on social media (e.g., election interference, vaccine hesitancy). Despite a burgeoning body of research on misinformation, it remains largely unclear who is susceptible to misinformation and why. To address this, we conducted a systematic individual participant data meta-analysis covering 256,337 unique choices made by 11,561 US-based participants across 31 experiments. Our meta-analysis reveals the impact of key demographic and psychological factors on online misinformation veracity judgments. We also disentangle the ability to discern between true and false news (discrimination ability) from response bias, that is, the tendency to label news as either true (true-news bias) or false (false-news bias). Across all studies, participants were well above-chance accurate for both true (68.51%) and false (67.24%) news headlines. We find that older age, higher analytical thinking skills, and identifying as a Democrat are associated with higher discrimination ability. Additionally, older age and higher analytical thinking skills are associated with a false-news bias (caution). In contrast, ideological congruency (alignment of participants’ ideology with news), motivated reflection, and self-reported familiarity with news are associated with a true-news bias (naïvety). Displaying sources alongside news headlines is associated with improved discrimination ability, with Republicans benefiting more from source display. Our results provide critical insights that can help inform the design of targeted interventions.

False news—given its capacity to distort public opinion and erode trust—has prompted extensive research on potential countermeasures. Yet, there has been no systematic, comparative, and computational investigation of false news sharing and how best to curb it. To address this gap, we apply a semi-integrative experimental approach that (a) compares multiple existing false news interventions, (b) examines how individual and news-level factors predict false news sharing and shape intervention efficacy, and (c) uses drift-diffusion modeling to uncover the decision-making processes underlying all these effects. We find warning labels and media literacy tips to substantially improve news-sharing quality, whereas social norm cues exert a comparatively modest effect, and accuracy prompts yield only subtle benefits. Although numerous individual factors (e.g., age, political conservatism, social media use) predicted news-sharing quality, the observed intervention effects remained broadly robust across these factors, proving effective even within at-risk populations. Intervention outcomes were likewise robust to news-level variation, such as the believability, sensationalism, and political congruence of news content. Despite this robustness, we find each intervention to operate via distinct decision-making pathways. Warning labels shift initial sharing intentions toward sharing higher quality news, whereas media literacy tips operate later, enhancing the processing of news content and increasing cautiousness before making sharing decisions. By applying a multicomponent experimental framework, this work clarifies the risk factors and decision-making processes driving false news sharing and pinpoints which interventions work best, how they operate at the process level, and in which contexts they should be most effective.