Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV Shows

TL;DR

This paper explores using prompt-engineered large language models to automate framing analysis in TV shows, aiming to support media literacy and journalism through AI-assisted content interpretation.

Contribution

It introduces a novel method leveraging prompt-engineering LLMs for framing detection in television content, demonstrating initial agreement with human analysis.

Findings

Agreement rate of up to 43% between LLMs and humans

Potential for refinement and improvement of the approach

Applications in journalism, education, and interactive media

Abstract

In the current media landscape, understanding the framing of information is crucial for critical consumption and informed decision making. Framing analysis is a valuable tool for identifying the underlying perspectives used to present information, and has been applied to a variety of media formats, including television programs. However, manual analysis of framing can be time-consuming and labor-intensive. This is where large language models (LLMs) can play a key role. In this paper, we propose a novel approach to use prompt-engineering to identify the framing of spoken content in television programs. Our findings indicate that prompt-engineering LLMs can be used as a support tool to identify frames, with agreement rates between human and machine reaching up to 43\%. As LLMs are still under development, we believe that our approach has the potential to be refined and further improved. The potential of this technology for interactive media applications is vast, including the development of support tools for journalists, educational resources for students of journalism learning about framing and related concepts, and interactive media experiences for audiences.