A Structured Clustering Approach for Inducing Media Narratives
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.
Cite
@article{arxiv.2604.10368,
title = {A Structured Clustering Approach for Inducing Media Narratives},
author = {Rohan Das and Advait Deshmukh and Alexandria Leto and Zohar Naaman and I-Ta Lee and Maria Leonor Pacheco},
journal= {arXiv preprint arXiv:2604.10368},
year = {2026}
}
Comments
Accepted to the Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)