Flexible Quantification of Issue-framing at Scale with LLMs

View a PDF of the paper titled Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs, by Simon D Angus and Lachlan O’Neill

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Abstract:Detecting issue framing in text – how different perspectives approach the same topic – is valuable for social science and policy analysis, yet challenging for automated methods due to subtle linguistic differences. We introduce `paired completion’, a novel approach using LLM next-token log probabilities to detect contrasting frames using minimal examples. Through extensive evaluation across synthetic datasets and a human-labeled corpus, we demonstrate that paired completion is a cost-efficient, low-bias alternative to both prompt-based and embedding-based methods, offering a scalable solution for analyzing issue framing in large text collections, especially suited to low-resource settings.