Rethinking news framing with large language models.

ABSTRACT: 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 thes

SUBMITTER: Tohidi A 

PROVIDER: S-EPMC12753771 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature