Framing by Wording, Framing by Selection: A Large-Scale Two-Dimensional Audit of French News Headlines, 2022-2025

A two-dimensional framework separating salience framing from selection framing was applied to 902,111 deduplicated French news headlines from 25 outlets spanning 2022-2025, according to an arXiv paper (2609.28487v1). The study built a 10,000-headline French supervision set using three LLM annotators with majority-vote resolution and human arbitration, validated against two annotator-independent blind human studies. The authors report that salience and selection divergence are positively correlated yet leave nearly half of outlet-level variance unexplained, that default classification thresholds systematically inflate corpus-level salience estimates, and that headlines mentioning Jews, the Far-right, and Muslims carry the highest detected salience rates; the supervision set, lexicons, and analysis code are released.

arXiv:2609.28487v1 Announce Type: new Abstract: News headlines frame public issues both by what they select and by how they word it, yet computational framing work typically collapses these operations into a single score. We introduce a two-dimensional framework that separates salience framing, measured through four wording devices loaded vocabulary, blame attribution, threat framing, rhetorical question , from selection framing, measured through outlet-level story-form and high-charge distributions. We build a 10,000-headline French supervision set using three LLM annotators with majority-vote resolution and human arbitration, validate the labels against two annotator-independent blind human studies, and apply the strongest classifier to 902,111 deduplicated headlines from 25 French outlets 2022-2025 . Three main findings emerge. First, salience and selection divergence are positively correlated yet leave nearly half of outlet-level variance unexplained, populating interpretively distinct off-diagonal cells in a four-cell outlet typology. Second, default classification thresholds systematically inflate corpus-level salience estimates; a precision-floor recalibration protocol corrects this distortion. Third, group-mention analysis reveals sharply unequal salience contexts: headlines mentioning Jews, the Far-right, and Muslims carry the highest detected salience rates, which broad event-context composition does not fully explain residuals are descriptive, not same-event causal estimates; per-group lexicon precision is reported alongside . To our knowledge, this is the largest framing-focused French headline audit to date; we release the supervision set, lexicons, and analysis code.