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This paper introduces a two-dimensional framework for analyzing news headlines, separating salience framing from selection framing, and applies it to a large-scale audit of French headlines using LLM-based annotation, revealing disparities in media framing.
This paper empirically evaluates how well LLMs align with human emotional perception of news framing, using a YouGov survey of 3,011 UK adults and seven LLMs assessing sympathy in headlines. It finds that alignment varies across models and demographic subgroups, highlighting the importance of differential alignment for AI development.
Experimental study shows inserting first/second-person pronouns into headlines has mixed effects on human memorability and that LLMs often produce inaccurate or unnatural revisions.