Academic Research

Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models, there is limited evidence of who or what influences the models themselves, leading to a flurry of concerns about which companies and governments build and regulate the models. Here we show through six studies that government control of the media across the world already influences the output of LLMs via their training data. We use a cross-national audit to show that LLMs exhibit a stronger pro-government valence when prompted in the languages of countries with lower media freedom than in those with higher media freedom. This result is correlational, so to triangulate the specific mechanism of how state media control can influence LLMs, we develop a multi-part case study on China’s media. We demonstrate that media scripted and curated by the Chinese state appears in LLM training datasets. To evaluate the plausible effect of this inclusion, we use an open-weight model to show that additional pretraining on Chinese state-coordinated media generates more positive answers to prompts about Chinese political institutions and leaders. We link this phenomenon to commercial models through two audit studies demonstrating that prompting models in Chinese generates more positive responses about China’s institutions and leaders than do the same queries in English. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output.

This forthcoming edited volume (Cambridge University Press) examines the transformative impact of artificial intelligence on democratic institutions, political behavior, governance, and the discipline of political science itself. The volume represents the report of the American Political Science Association’s Presidential Task Force on AI, Politics, and Political Science, co-chaired by Joshua Tucker and Nathaniel Persily. 

Across twelve chapters produced by close to 60 scholars, the report evaluates how generative AI and machine learning systems are reshaping public opinion formation, political communication, labor markets, electoral processes, state capacity, and regulatory frameworks. The authors analyze both the opportunities and risks posed by AI technologies, including concerns surrounding information integrity, ideological personalization, surveillance, democratic accountability, and concentrated technological power. Themes that cut across multiple chapters include: the unprecedented power of a small number of AI corporations; the opacity and non-replicability of model outputs; bias in AI systems; and the absence of agreed-upon benchmarks for evaluation.The volume also addresses methodological and ethical implications for political science research, emphasizing transparency, reproducibility, and the responsible integration of AI tools into scholarly inquiry. Ultimately, the volume argues that AI will not only alter political institutions and citizen-state relations, but also may fundamentally reshape how political knowledge is produced and interpreted. It calls for sustained interdisciplinary collaboration and evidence-based governance to ensure that AI development supports democratic resilience rather than undermining it.

Deceptive online networks are coordinated efforts that use identity deception to pursue strategic political or financial goals. During the US 2020 elections, these networks reached at least 37 million Facebook and 3 million Instagram users, representing 15% and 2% of the platforms’ active US adult users, respectively. Only 3 networks out of 49—1 network with explicitly political aims and 2 that appeared to use politics as a lure for profit—were responsible for over 70% of users reached. Notably, accounts unaffiliated with the networks played an important role in facilitating this reach by resharing content the three networks produced. Deceptive networks, regardless of whether their goals were political or financial, reached users who were older, more conservative, more frequently exposed to content from untrustworthy sources, and spent more time on Facebook.

Generative artificial intelligence agents are becoming increasingly active participants in conversations on social media platforms, yet little is known about how they shape public discussion of social problems. We present two preregistered online experiments testing AI-generated summaries in simulated, interactive social media environments. AI summaries increased the quality of user comments, without systematically increasing toxicity or negative affect. At the same time, AI exposure reduced engagement with conversation threads. AI summaries also increased the semantic similarity between user comments and the AI-generated summaries, suggesting that these systems function as informational anchors that shape discussion. Together, the findings reveal a tradeoff: AI-generated summaries can improve conversation quality while narrowing conversational engagement and channeling how users articulate political arguments. These results speak to growing concerns about how embedded AI systems fundamentally alter platform dynamics and shape public discourse.

What happens to information environments when democracies ban social media platforms? While a large literature examines information control under authoritarianism, democratic governments have increasingly intervened in major online platforms. We study a prominent case: Brazil’s 2024 national ban on the social media platform X. Using an event-study design, we estimate the causal effects of the ban and examine how partisan identity shaped responses. Drawing on a large sample of politically engaged users and ideal-point estimates of ideology, we find strong partisan asymmetries. Conservative users not aligned with the government were more likely to circumvent the ban, and right-leaning news domains became markedly more prevalent on the platform. We describe this dynamic as a “sorting ratchet”: the ban segmented the digital public sphere along partisan lines, with effects that persisted even after restrictions were lifted. Platform bans in democratic settings may therefore deepen polarization and durably reshape information environments

Large language models (LLMs) are increasingly used as synthetic survey respondents, yet it is unclear whether their belief-system structure matches that of real publics. We compare 28 LLMs to the 2024 General Social Survey (GSS) using 52 attitude items and demographic persona traits. We estimate polychoric correlation matrices and propagate un-certainty in the GSS via bootstrap resampling with multiple imputation. Constraint is measured by the variance share explained by the first principal component and by effective dependence, a determinant-based measure of global linear dependence. Across models, LLM personas exhibit substantially higher constraint than humans; conditioning on persona traits reduces constraint far more for LLMs, indicating greater demographic mediation. Projection onto a shared GSS basis further shows overemphasis of the leading dimension and missing secondary structure. These results caution against treating LLM personas as a reliable foundation for synthetic survey data generation.

Starting in January 2023, Louisiana and more than 20 other states passed laws requiring age verification for websites with substantial adult content. Using Google Trends data and a synthetic control design, we examine how these laws affect the public’s digital behavior across four dimensions: searches for compliant websites, non-compliant websites, VPNs, and adult content. Three months after the laws were passed, results show a 51% decrease in searches for the main compliant platform, while searches increased for both non-compliant platform (48.1%) and VPN services (23.6%). Through multiverse analyses, we demonstrate the robustness of these findings to numerous model specifications. Our findings reveal that while regulations reduce traffic to compliant sites and likely decrease overall consumption, users adapt by shifting to providers without verification requirements. This approach provides valuable insights for policymakers around the world considering similar legislative measures of digital content regulation. Our methodology also offers a framework for real-time policy evaluation in contexts with staggered implementation.

More than half of the world’s population uses social media. There is widespread debate among the public, politicians, and academics about social media’s impact on important outcomes, such as intergroup conflict and well-being. However, most prior research on the impact of social media relies on samples from the United States and Western Europe, despite emerging evidence suggesting that the impact of social media is likely to differ across the globe. Building on the results of pilot experiments from three countries (n = 894), we plan to conduct a global field experiment to measure the causal impact of reducing social media usage for two weeks across 23 countries (projected n > 8,000). We will then test how social media reduction influences four main outcomes: news knowledge, exposure to online hostility, intergroup attitudes, and well-being. We will also explore how the effects of social media reduction vary across world regions, focusing on three theoretically-informed country-level moderators: levels of income, inequality, and democracy. This large-scale, high-powered field experiment, and the global dataset resulting from it, will offer rare causal evidence to inform ongoing debates about the impact of social media and how it varies around the world.

Online panels have become an important resource for research in political science, but the compensation offered to panelists incentivizes them to become “survey professionals,” raising concerns about data quality. We provide evidence on survey professionalism exploring three US samples of subjects who donated their browsing data, recruited via Lucid, YouGov, and Facebook (total  𝑛=3,886). Survey professionalism is common, but varies across samples: by our most conservative estimate, we find 1.7% of respondents on Facebook, 7.6% on YouGov, and 34 7% on Lucid to be professionals (under the assumption that professionals are as likely as non-professionals to donate data after conditioning on observable demographics available from all online survey takers). However, evidence that professionals lower data quality is limited: they do not systematically differ demographically or politically from non-professionals and do not exhibit more response instability. They are, however, somewhat more likely to speed, straightline, and attempt to take questionnaires repeatedly. To address potential selection issues in donating of browsing data, we present sensitivity analyses with lower bounds for survey professionalism. While concerns about professionalism are warranted, we conclude that survey professionals do not, by and large, distort inferences of research based on online panels.

Scholarship has identified key determinants of people’s belief in misinformation predominantly from English-language contexts. However, multilingual citizens often consume news media in multiple languages. We study how the language of consumption affects belief in misinformation and true news articles in multilingual environments. We suggest that language may pass on specific cues affecting how bilinguals evaluate information. In a ten-week survey experiment with bilingual adults in Ukraine, we measured if subjects evaluating information in their less-preferred language were less likely to believe it. We find those who prefer Ukrainian are less likely to believe both false and true stories written in Russian by approximately 0.2 standard deviation units. Conversely, those who prefer Russian show increased belief in false stories in Ukrainian, though this effect is less robust. A secondary digital media literacy intervention does not increase discernment as it reduces belief in both true and false stories equally.

In most advanced democracies, concerns about the spread of misinformation are typically associated with feed-based social media platforms like Twitter and Facebook. These platforms also account for the vast majority of research on the topic. However, in most of the world, particularly in Global South countries, misinformation often reaches citizens through social media messaging apps, particularly WhatsApp. To fill the resulting gap in the literature, we conducted a multimedia deactivation experiment to test the impact of reducing exposure to potential sources of misinformation on WhatsApp during the weeks leading up to the 2022 Presidential election in Brazil. We find that this intervention significantly reduced participants’ recall of false rumors circulating widely during the election. However, consistent with theories of mass media minimal effects, a short-term change in the information environment did not lead to significant changes in belief accuracy, political polarization, or well-being.

Social media is used by millions of Americans to access news and politics. Yet there are no studies, to date, examining whether these behaviors systematically vary for those whose political incorporation process is distinct from those in the majority. We fill this void by examining how Latino online political activity compares to that of white Americans and the role of language in Latinos’ online political engagement. We hypothesize that Latino online political activity is comparable to whites. Moreover, given media reports suggesting that greater quantities of political misinformation are circulating on Spanish versus English-language social media, we expect reliance on Spanish-language social media for news predicts beliefs in inaccurate political narratives. Our survey findings, which we believe to be the largest original survey of the online political activity of Latinos and whites, reveal support for these expectations. Latino social media political activity, as measured by sharing/viewing news, talking about politics, and following politicians, is comparable to whites, both in self-reported and digital trace data. Latinos also turned to social media for news about COVID-19 more often than did whites. Finally, Latinos relying on Spanish-language social media usage for news predicts beliefs in election fraud in the 2020 U.S. Presidential election.

Social media creates the possibility for rapid, viral spread of content, but how many posts actually reach millions? And is misinformation special in how it propagates? We answer these questions by analyzing the virality of and exposure to information on Facebook during the U.S. 2020 presidential election. We examine the diffusion trees of the approximately 1 B posts that were re-shared at least once by U.S.-based adults from July 1, 2020, to February 1, 2021. We differentiate misinformation from non-misinformation posts to show that (1) misinformation diffused more slowly, relying on a small number of active users that spread misinformation via long chains of peer-to-peer diffusion that reached millions; non-misinformation spread primarily through one-to-many affordances (mainly, Pages); (2) the relative importance of peer-to-peer spread for misinformation was likely due to an enforcement gap in content moderation policies designed to target mostly Pages and Groups; and (3) periods of aggressive content moderation proximate to the election coincide with dramatic drops in the spread and reach of misinformation and (to a lesser extent) political content.

False political narratives are nearly inescapable on social media in the United States. They are a particularly acute problem for Latinos, and especially for those who rely on Spanish-language social media for news and information. Studies have shown that Latinos are vulnerable to misinformation because they rely more heavily on social media and messaging platforms than non-Hispanic whites. Moreover, fact-checking algorithms are not as robust in Spanish as they are in English, and social media platforms put far more effort into combating misinformation on English-language media than Spanish-language media, which compounds the likelihood of being exposed to misinformation. As a result, we expect that Latinos who use Spanish-language social media to be more likely to believe in false political narratives when compared with Latinos who primarily rely on English-language social media for news. To test this expectation, we fielded the largest online survey to date of social media usage and belief in political misinformation of Latinos. Our study, fielded in the months leading up to and following the 2022 midterm elections, examines a variety of false political narratives that were circulating in both Spanish and English on social media. We find that social media reliance for news predicts one’s belief in false political stories, and that Latinos who use Spanish-language social media have a higher probability of believing in false political narratives, compared with Latinos using English-language social media.

Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

In most advanced democracies, concerns about the spread of misinformation are typically associated with feed-based social media platforms like Twitter and Facebook. These platforms also account for the vast majority of research on the topic. However, in most of the world, particularly in Global South countries, misinformation often reaches citizens through social media messaging apps, particularly WhatsApp. To fill the resulting gap in the literature, we conducted a multimedia deactivation experiment to test the impact of reducing exposure to potential sources of misinformation on WhatsApp during the weeks leading up to the 2022 Presidential election in Brazil. We find that this intervention significantly reduced participants’ exposure to false rumors circulating widely during the election. However, consistent with theories of mass media minimal effects, a short-term reduction in exposure to misinformation ahead of the election did not lead to significant changes in belief accuracy, political polarization, or well-being.

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

Online data is key to understanding the leadup to the January 6 insurrection, including how and why election fraud conspiracies spread online, how conspiracy groups organized online to participate in the insurrection, and other factors of online life that led to the insurrection. However, there are significant challenges in accessing data for this research. First, platforms restrict which researchers get access to data, as well as what researchers can do with the data they access. Second, this data is ephemeral; that is, once users or the platform remove the data, researchers can no longer access it. These factors affect what research questions can ever be asked and answered.

Considerable scholarly attention has been paid to understanding belief in online misinformation, with a particular focus on social networks. However, the dominant role of search engines in the information environment remains underexplored, even though the use of online search to evaluate the veracity of information is a central component of media literacy interventions. Although conventional wisdom suggests that searching online when evaluating misinformation would reduce belief in it, there is little empirical evidence to evaluate this claim. Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them. To shed light on this relationship, we combine survey data with digital trace data collected using a custom browser extension. We find that the search effect is concentrated among individuals for whom search engines return lower-quality information. Our results indicate that those who search online to evaluate misinformation risk falling into data voids, or informational spaces in which there is corroborating evidence from low-quality sources. We also find consistent evidence that searching online to evaluate news increases belief in true news from low-quality sources, but inconsistent evidence that it increases belief in true news from mainstream sources. Our findings highlight the need for media literacy programmes to ground their recommendations in empirically tested strategies and for search engines to invest in solutions to the challenges identified here.

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper 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.

Despite broad adoption of digital media literacy interventions that provide online users with more information when consuming news, relatively little is known about the effect of this additional information on the discernment of news veracity in real time. Gaining a comprehensive understanding of how information impacts discernment of news veracity has been hindered by challenges of external and ecological validity. Using a series of pre-registered experiments, we measure this effect in real time. Access to the full article relative to solely the headline/lede and access to source information improves an individual's ability to correctly discern the veracity of news. We also find that encouraging individuals to search online increases belief in both false/misleading and true news. Taken together, we provide a generalizable method for measuring the effect of information on news discernment, as well as crucial evidence for practitioners developing strategies for improving the public's digital media literacy.

Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.

Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta’s Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook’s news ecosystem than those favored by liberals.