From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation

TL;DR

This paper introduces a qualitative content analysis-based framework for annotating economic narratives as causal graphs, improving annotation quality and reliability in NLP tasks involving narrative interpretation.

Contribution

It presents a novel graph annotation framework for narratives, evaluates annotation quality with a factorial design, and offers practical guidance for reducing variability in narrative graph annotation.

Findings

Lenient metrics overestimate annotation reliability.

Locally-constrained representations reduce annotation variability.

Open-source tools for graph-based Krippendorff's alpha are provided.

Abstract

Narratives in news discourse play a critical role in shaping public understanding of economic events, such as inflation. Annotating and evaluating these narratives in a structured manner remains a key challenge for Natural Language Processing (NLP). In this work, we introduce a narrative graph annotation framework that integrates principles from qualitative content analysis (QCA) to prioritize annotation quality by reducing annotation errors. We present a dataset of inflation narratives annotated as directed acyclic graphs (DAGs), where nodes represent events and edges encode causal relations. To evaluate annotation quality, we employed a $6\times3$ factorial experimental design to examine the effects of narrative representation (six levels) and distance metric type (three levels) on inter-annotator agreement (Krippendorrf's $\alpha$), capturing the presence of human label variation (HLV) in narrative interpretations. Our analysis shows that (1) lenient metrics (overlap-based distance) overestimate reliability, and (2) locally-constrained representations (e.g., one-hop neighbors) reduce annotation variability. Our annotation and implementation of graph-based Krippendorrf's $\alpha$ are open-sourced. The annotation framework and evaluation results provide practical guidance for NLP research on graph-based narrative annotation under HLV.