A Causal Graph Approach to Oppositional Narrative Analysis

• URL: http://arxiv.org/abs/2603.06135v2
• Date: Tue, 10 Mar 2026 11:32:35 GMT
• Title: A Causal Graph Approach to Oppositional Narrative Analysis
• Abstract summary: We propose a graph-based framework for the detection, analysis, and classification of oppositional narratives.<n>Our approach derives a causal representation of each contribution to the final classification by distilling a graph into a minimal subgraph.<n>Building upon this representation, we introduce a classification pipeline that outperforms existing approaches to oppositional thinking classification.
• Score: 0.3811184252495268
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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