Rethinking news framing with large language models

Mainstream media, with its broad reach, plays a central role in shaping public opinion and thus warrants close scrutiny. Subtle forms of media bias-such as selective fact presentation and tone-can meaningfully influence public attitudes, even when reporting remains factually accurate. Although effects such as these have been widely studied by scholars of framing, much of the existing research focuses on specific topics and relies on manually constructed or pre-existing frames, limiting both scalability and generalizability. Here we introduce a novel framework that leverages large language models (LLMs) to generate synthetic news articles by systematically varying the selection and tone of the content while holding factual accuracy and other features constant. We evaluate the impact of these alternative framings in a large, pre-registered randomized experiment (N = 2,141), and find that selective presentation of accurate information can significantly shift individuals' policy views and emotional responses across a diverse collection of topics. These effects are consistently stronger for negative than positive framings and are more pronounced among individuals who say they are less informed about the topic. Our findings demonstrate the persuasive power of subtle bias in mainstream news as well as the value of LLMs as tools for scalable, controlled investigations of media effects.