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.

翻译：当前的文本分析方法依赖于在预定义本体中标注的数据，往往将人类偏见嵌入黑盒模型中。尽管这些方法取得了近乎完美的性能，但它们利用的是非结构化、线性的模式识别，而非对话语中自然出现的实体间结构化交互进行建模。在本研究中，我们提出了一种基于图的框架，通过将叙事表示为实体交互图，用于对抗性叙事及其底层实体的检测、分析和分类。此外，通过在节点层面引入因果估计，我们的方法通过将构建的句子图提炼为最小因果子图，推导出每个贡献对最终分类的因果表征。基于此表征，我们引入了一种分类流程，其在对抗性思维分类任务上的表现优于现有方法。