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        <title>From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation</title>
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    <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 enhance methodological consistency. 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 α), capturing the presence of human label variation (HLV) in narrative interpretations. Our analysis shows that (1) lenient metrics (overlap-based distance) overestimate reliability; (2) locally-constrained representations (e.g., one-hop neighbors) reduce annotation variability. Our annotation and implementation of graph-based Krippendorrf’s α are open-sourced. The annotation framework and evaluation results provide practical guidance for NLP research on graph-based narrative annotation.</abstract>
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%0 Conference Proceedings
%T From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation
%A Huang, Junbo
%A Weinig, Max
%A Fritsche, Ulrich
%A Usbeck, Ricardo
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F huang-etal-2026-variance
%X 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 enhance methodological consistency. 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 α), capturing the presence of human label variation (HLV) in narrative interpretations. Our analysis shows that (1) lenient metrics (overlap-based distance) overestimate reliability; (2) locally-constrained representations (e.g., one-hop neighbors) reduce annotation variability. Our annotation and implementation of graph-based Krippendorrf’s α are open-sourced. The annotation framework and evaluation results provide practical guidance for NLP research on graph-based narrative annotation.
%R 10.63317/2chc9vvj5kxc
%U https://aclanthology.org/2026.lrec-1.74/
%U https://doi.org/10.63317/2chc9vvj5kxc
%P 958-972

Markdown (Informal)

[From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation](https://aclanthology.org/2026.lrec-1.74/) (Huang et al., LREC 2026)

• From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation (Huang et al., LREC 2026)

ACL

• Junbo Huang, Max Weinig, Ulrich Fritsche, and Ricardo Usbeck. 2026. From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 958–972, Palma de Mallorca, Spain. ELRA Language Resource Association.