[Submitted on 12 Aug 2026]
Title:Framing by Wording, Framing by Selection: A Large-Scale Two-Dimensional Audit of French News Headlines, 2022-2025
View a PDF of the paper titled Framing by Wording, Framing by Selection: A Large-Scale Two-Dimensional Audit of French News Headlines, 2022-2025, by Amr Sobhy
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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.
Comments: 20 pages, 1 figure, includes appendices. Accepted for oral presentation at ICNLSP 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.28487 [cs.CL]
(or arXiv:2609.28487v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.28487
arXiv-issued DOI via DataCite
Submission history
From: Amr Sobhy [view email] [v1] Wed, 12 Aug 2026 20:33:09 UTC (532 KB)
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