A systematic workflow for utilizing and evaluating large language models in public health policy framing analysis

Background

For public health policy research, it is crucial to monitor policy debates to inform evidence-based policymaking. One popular method is to analyze the news for salient frames used to argue for or against a controversial topic. However, this method is time and resource-intensive since domain experts need to manually analyze relevant articles to identify frames. This study presents an approach for using large language models (LLMs) to extract frames and to systematically evaluate results.