State Media Monitor Global Dataset 2025

Replication Data for "State Media Control Influences Large Language Models"

Replication dataset for "State Media Control Influences Large Language Models," forthcoming in Nature (https://doi.org/10.1038/s41586-026-10506-7). We show through six studies that government control of the media across the world influences the output of large language models (LLMs) via their training data.

Replication Data for "State Media Control Influences Large Language Models"

Replication dataset for "State Media Control Influences Large Language Models," forthcoming in Nature (https://doi.org/10.1038/s41586-026-10506-7). We show through six studies that government control of the media across the world influences the output of large language models (LLMs) via their training data.

Media Influence Matrix – The World's Most Reliable Influence Tracker

This week’s edition reads five FY2025 reports against each other: Dnevnik (Slovenia), Delfi Latvia, Phoenix New Media (China), Hanza Media (Croatia) and Digi Communications (Romania). It finds two publishers separating their journalism from their balance sheets, a Croatian publisher closing a 35-year-old political weekly on a 1.1% net margin, a Chinese state-linked digital news group whose financial centre has quietly shifted from advertising to mini-program reading apps, and a Romanian telecom giant’s having its news channel walk off the must-carry list.

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State media control influences large language models

Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models1–10, 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 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.

State media control influences large language models

Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models1–10, 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 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.

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State Media Control Influences Large Language Models – State Media & LLMs

Hannah Waight1,2, Eddie Yang1,3, Yin Yuan4, Solomon Messing5, Margaret E. Roberts4, Brandon M. Stewart6, Joshua A. Tucker5,7

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