A Structured Clustering Approach for Inducing Media Narratives

2026-04-11

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.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Machine Learning: Unsupervised & Self-Supervised Learning

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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

• media narratives
• narrative structures
• structured clustering
• framing theory
• scalability

Innovation

• structured clustering
• narrative schema induction
• media narratives
• event-character modeling
• framing theory