Narrative Media Framing in Political Discourse
This work addresses the gap in automated analysis of narrative framing for researchers and analysts in political communication, though it is incremental as it builds on existing framing theory.
The paper tackles the problem of automated narrative frame analysis in political discourse by formalizing narrative framing and applying it to climate change and COVID-19 domains, resulting in a released dataset and tests showing LLMs can predict narrative frames with generalizability across domains.
Narrative frames are a powerful way of conceptualizing and communicating complex, controversial ideas, however automated frame analysis to date has mostly overlooked this framing device. In this paper, we connect elements of narrativity with fundamental aspects of framing, and present a framework which formalizes and operationalizes such aspects. We annotate and release a data set of news articles in the climate change domain, analyze the dominance of narrative frame components across political leanings, and test LLMs in their ability to predict narrative frames and their components. Finally, we apply our framework in an unsupervised way to elicit components of narrative framing in a second domain, the COVID-19 crisis, where our predictions are congruent with prior theoretical work showing the generalizability of our approach.