A Structured Clustering Approach for Inducing Media Narratives
🤖 AI Summary
Existing approaches to media narrative analysis often struggle to balance fine-grained detail with scalability, either sacrificing nuance through coarse-grained modeling or relying on domain-specific annotations that limit generalizability. This work proposes an unsupervised method that jointly models events and characters and incorporates structured clustering to automatically induce interpretable narrative schemata from large-scale news corpora. By avoiding manual annotation, the approach yields narrative frameworks that align with theoretical expectations while demonstrating strong generalization capabilities. The resulting schemata preserve fine-grained semantic structure and enable efficient, interpretable discovery of media narratives across diverse datasets.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
media narratives
narrative structures
structured clustering
framing theory
scalability
Innovation
Methods, ideas, or system contributions that make the work stand out.
structured clustering
narrative schema induction
media narratives
event-character modeling
framing theory
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