用因果图分析话语对立，突破传统文本模型的偏见与线性模式
A Causal Graph Approach to Oppositional Narrative Analysis
- 将叙事建模为实体交互图，捕捉话语中自然出现的结构关系
- 通过节点级因果估计，提炼出影响分类结果的关键因果子图
- 在对立思维分类任务上超越现有方法，适合舆情分析与社会科学研究
当前文本分析方法依赖预定义本体标注的数据，常在黑箱模型中嵌入人为偏见。尽管性能接近完美，但这些方法仅依赖非结构化的线性模式识别，而非建模话语中自然涌现的实体间结构化互动。本文提出一种基于图的框架，将叙事表示为实体-交互图，用于对立叙事及其潜在实体的检测、分析与分类。通过在节点层面引入因果估计，从构建的句子图中提炼出最小因果子图，实现对每个贡献项的因果表征。基于此表示，我们设计的分类流水线在对立思维分类任务上优于现有方法。
原文摘要 · Abstract (English)
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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