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.
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