A Causal Graph Approach to Oppositional Narrative Analysis
Abstract
Current methods for textual analysis rely on data annotated within predefined ontologies, often embedding human bias within black-box models. Despite achieving near-perfect performance, these approaches exploit unstructured, linear pattern recognition rather than modeling the structured interactions between entities that naturally emerge in discourse. In this work, we propose a graph-based framework for the detection, analysis, and classification of oppositional narratives and their underlying entities by representing narratives as entity-interaction graphs. Moreover, by incorporating causal estimation at the node level, our approach derives a causal representation of each contribution to the final classification by distilling the constructed sentence graph into a minimal causal subgraph. Building upon this representation, we introduce a classification pipeline that outperforms existing approaches to oppositional thinking classification task.
Cite
@article{arxiv.2603.06135,
title = {A Causal Graph Approach to Oppositional Narrative Analysis},
author = {Diego Revilla and Martin Fernandez-de-Retana and Lingfeng Chen and Aritz Bilbao-Jayo and Miguel Fernandez-de-Retana},
journal= {arXiv preprint arXiv:2603.06135},
year = {2026}
}