A Hierarchical Error Framework for Reliable Automated Coding in Communication Research: Applications to Health and Political Communication

TL;DR

The paper tackles reliability and validity challenges in automated content analysis for communication research by introducing the Hierarchical Error Correction (HEC) framework, which treats model failures as layered errors (knowledge, reasoning, complexity) and prescribes a three-phase procedure: error profiling, targeted interventions, and rigorous validation. It demonstrates the approach across four domain studies (health, law, politics) using five diverse LLMs, showing consistent accuracy gains predominantly in moderate-to-high baseline tasks and identifying boundary conditions where gains diminish for very high-baseline or precision-matching tasks. The results reveal domain-specific error hierarchies, with knowledge-layer errors dominating health and politics and reasoning-layer errors dominating legal reasoning, informing tailored interventions like terminology normalization and boundary-judgment prompts. The framework emphasizes a measurement-first mindset, cross-model validation, and auditable reporting of reliability and validity, offering practical guidance for deploying automated coding in the social sciences while clarifying its limits and transfer potential across domains.

Abstract

Automated content analysis increasingly supports communication research, yet scaling manual coding into computational pipelines raises concerns about measurement reliability and validity. We introduce a Hierarchical Error Correction (HEC) framework that treats model failures as layered measurement errors (knowledge gaps, reasoning limitations, and complexity constraints) and targets the layers that most affect inference. The framework implements a three-phase methodology: systematic error profiling across hierarchical layers, targeted intervention design matched to dominant error sources, and rigorous validation with statistical testing. Evaluating HEC across health communication (medical specialty classification) and political communication (bias detection), and legal tasks, we validate the approach with five diverse large language models. Results show average accuracy gains of 11.2 percentage points (p < .001, McNemar's test) and stable conclusions via reduced systematic misclassification. Cross-model validation demonstrates consistent improvements (range: +6.8 to +14.6pp), with effectiveness concentrated in moderate-to-high baseline tasks (50-85% accuracy). A boundary study reveals diminished returns in very high-baseline (>85%) or precision-matching tasks, establishing applicability limits. We map layered errors to threats to construct and criterion validity and provide a transparent, measurement-first blueprint for diagnosing error profiles, selecting targeted interventions, and reporting reliability/validity evidence alongside accuracy. This applies to automated coding across communication research and the broader social sciences.

Figures (2)

• Figure 1: Cross-model validation. Five LLMs all improve under HEC (avg +11.2pp); gains diminish as baselines rise, with medium–large effects across models.
• Figure 2: HEC improvements across domains. Gains concentrate in moderate-to-high baseline tasks (50--85%); effectiveness diminishes for very high baselines ($>85\%$) or precision-matching tasks.