A Structured Clustering Approach for Inducing Media Narratives
For computational social science and media analysis, it addresses the bottleneck of capturing nuanced narrative structures at scale, but the novelty is incremental as it combines existing clustering techniques with domain theory.
The paper introduces a structured clustering framework that jointly models events and characters to induce media narrative schemas, achieving alignment with framing theory and scalability to large corpora without manual annotation.
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.