用结构化聚类挖掘媒体叙事模式，兼顾可解释性与大规模分析
A Structured Clustering Approach for Inducing Media Narratives
- 联合建模事件与人物，通过结构聚类发现叙事骨架
- 生成符合框架理论的可解释叙事结构，无需人工标注
- 适合研究舆论演化、传播分析的研究者使用
媒体叙事对塑造公众意见具有巨大影响力，但现有计算方法难以捕捉传播理论强调的细腻叙事结构。当前方法或因粗粒度分析而遗漏细微模式，或需依赖领域特定分类体系，限制了可扩展性。为此，我们提出一种联合建模事件与角色的结构聚类框架，用于自动推导丰富的叙事模板。该方法生成的叙事结构符合既有框架理论，同时可在无需详尽人工标注的情况下扩展至大规模语料，实现可解释性与规模化的平衡。
原文摘要 · Abstract (English)
Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.
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