A Causal Graph Approach to Oppositional Narrative Analysis

TL;DR

This paper introduces a graph-based causal framework for analyzing oppositional narratives, capturing structured entity interactions and causal influences to improve classification accuracy over existing methods.

Contribution

It presents a novel graph and causal estimation approach for narrative analysis, moving beyond traditional pattern recognition models.

Findings

Outperforms existing methods in oppositional thinking classification

Provides causal interpretability of narrative contributions

Effectively models entity interactions in discourse

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.