Framing by Wording, Framing by Selection: A Large-Scale Two-Dimensional Audit of French News Headlines, 2022-2025
Researchers propose a two-dimensional framework to decouple salience framing from selection framing in news headlines.
Current computational methods often conflate how news is worded with what is selected for coverage. This new framework separates salience (measured via loaded vocabulary, blame, and rhetorical devices) from selection (measured via outlet-level story distributions). The study utilizes a 10,000-headline French dataset annotated by LLMs to provide a more granular audit of media bias.