Revisiting Framing Codebooks with AI: Employing Large Language Models as Analytical Collaborators in Deductive Content Analysis

2026-04-21

AI Summary

This study addresses the limitations of traditional framing codebooks when applied to large-scale, cross-cultural, or dynamically evolving news corpora—namely, ambiguous rules, difficulty adjudicating boundary cases, and insufficient theoretical adaptability. To overcome these challenges, the authors propose an interactive workflow that integrates theory-driven guidance with data-driven insights, positioning large language models (LLMs) as analytical collaborators. Through iterative human–LLM dialogue, the approach refines codebooks, externalizes decision logic, and surfaces latent framing dimensions. While preserving researchers’ interpretive authority, this method enhances the creativity and adaptability of the coding process. Empirical application to Latin American news corpora demonstrates its capacity to identify novel framing patterns and effectively recalibrate established theoretical frameworks within new cultural and contextual settings.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Codebooks are central to framing research, providing theoretically grounded criteria for analyzing news content. While traditionally codebooks are built from theoretical frameworks and researchers' knowledge, applying these codebooks to large news corpora often exposes ambiguities, borderline cases, and underspecified rules that are difficult to resolve through theory alone. Moreover, news corpora evolve over time and differ across cultures, necessitating that researchers revisit the theoretical frameworks underlying these codebooks. In this article, we propose a workflow that uses Large Language Models (LLMs) to augment the creation and refinement of framing codebooks by combining theoretical frameworks with data-driven exploration. Rather than treating LLMs as automated classifiers, this approach positions them as analytic collaborators that help externalize decision rules, surface latent dimensions, and support iterative revisions of codebooks through dialogues between researchers and their data. We illustrate this workflow using a dataset of Latin American news coverage, demonstrating how the application of LLMs' capabilities has led to the surfacing of latent patterns, the generation of frame distinctions, and the adaptation of frameworks to new contexts. This method provides an LLM-assisted strategy that supports methodology creativity while preserving researchers' interpretative authority.

Problem

• codebooks
• framing analysis
• content analysis
• theoretical frameworks
• news corpora

Innovation

• Large Language Models
• Codebook Development
• Framing Analysis
• Analytical Collaboration
• Deductive Content Analysis