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

arXiv: https://arxiv.org/abs/2409.12561 · HTML: https://mlsanity.com/paper/2409.12561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://mlsanity.com/paper/2410.15308/) (2024)
- [Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation](https://mlsanity.com/paper/2504.02293/) (2025)
- [Red and blue language: Word choices in the Trump & Harris 2024 presidential debate](https://mlsanity.com/paper/2410.13654/) (2024)
- [Listening Between the Lines: Decoding Podcast Narratives with Language Modeling](https://mlsanity.com/paper/2511.05310/) (2025)
- [Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation](https://mlsanity.com/paper/2606.03761/) (2026)

## Abstract (arXiv)

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.