A Structured Clustering Approach for Inducing Media Narratives

arXiv cs.CL / 4/14/2026

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Key Points

The paper tackles the challenge of computationally extracting media narrative structures that align with communication theory and framing concepts.
It proposes an approach that jointly models events and characters using structured clustering to induce explainable narrative schemas.
Compared with prior methods, it aims to avoid missing subtle narrative patterns while also eliminating reliance on rigid, domain-specific taxonomies.
The authors report that the framework can scale to large media corpora without requiring exhaustive manual annotation, improving practical usability.

Abstract

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.