Public Opinion

White nationalism has become increasingly visible in American public life. Yet we lack knowledge of the predictors and prevalence of explicit support for this ideology, as survey research has treated it as a latent construct captured through indirect indicators. We introduce and validate a measure that informs respondents of the core tenets of the white nationalist movement and then asks whether they support the movement. Expressed support is distinct from organizational affiliation with the movement, which we do not measure. Here we show with three national surveys of non-Hispanic white adults (conducted from 2021 to 2025) that support for white nationalism is (1) higher among white people encountering personal hardship and local social distress, consistent with strain theories3,16; (2) unrelated to local racial composition or recent demographic change, complicating status threat accounts4,17,18; and (3) elevated among white people with close friendships primarily online, consistent with theories of extremism driven by online mobilization7,10,19. Support for white nationalism is more common among white people with lower incomes and education, Republicans and conservatives, and those who are politically disaffected. In our national probability sample, prevalence of support was estimated at 4.9% of white adults, including 13.5% of white men aged 18–29. Support for white nationalism seems to constitute a discrete dimension of white racial attitudes, and many white people who endorse its core ideas are unaware of the label.

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.