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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.06135 |
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| _version_ | 1866915848417968128 |
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| author | Revilla, Diego Fernandez-de-Retana, Martin Chen, Lingfeng Bilbao-Jayo, Aritz Fernandez-de-Retana, Miguel |
| author_facet | Revilla, Diego Fernandez-de-Retana, Martin Chen, Lingfeng Bilbao-Jayo, Aritz Fernandez-de-Retana, Miguel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Causal Graph Approach to Oppositional Narrative Analysis Revilla, Diego Fernandez-de-Retana, Martin Chen, Lingfeng Bilbao-Jayo, Aritz Fernandez-de-Retana, Miguel Computation and Language Artificial Intelligence 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. |
| title | A Causal Graph Approach to Oppositional Narrative Analysis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06135 |