A Causal Graph Approach to Oppositional Narrative Analysis
This work addresses the problem of biased and unstructured textual analysis for researchers and analysts studying narrative dynamics, offering a more structured and interpretable approach.
The paper introduces a graph-based framework to analyze and classify oppositional narratives by representing them as entity-interaction graphs. This method incorporates causal estimation to derive a minimal causal subgraph for each contribution, leading to a classification pipeline that outperforms existing approaches in opposational thinking classification.
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.