Text and Content Analysis

What political content do we pay attention to online? Diverse political information is essential for democratic competence, yet online media raises concerns about fragmented information diets. Research on selective exposure highlights how social media can foster ideological echo chambers, while other studies emphasize incidental exposure to diverse viewpoints. A critical limitation is measurement: existing research primarily uses engagement metrics (e.g., likes or shares), neglecting passive exposure or attention—what users notice but do not interact with. In this study, we address this gap through an experimental platform that separately records attention and engagement. Our findings indicate that the ideology-engagement association is about seven times the magnitude of the ideology-attention association. This underscores the importance of measuring attention, rather than solely engagement, to accurately assess the diversity of online information diets.

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.