A Structured Clustering Approach for Inducing Media Narratives

TL;DR

This paper introduces a structured clustering framework that induces explainable media narratives by modeling events and characters, aligning with framing theory and scalable to large datasets.

Contribution

It presents a novel approach for inducing rich, explainable narrative schemas that capture nuanced storytelling structures without extensive manual annotation.

Findings

Produces narrative schemas aligned with framing theory

Scales to large corpora efficiently

Captures subtle narrative patterns

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.