@inproceedings{das-etal-2026-structured,
title = "A Structured Clustering Approach for Inducing Media Narratives",
author = "Das, Rohan and
Deshmukh, Advait and
Leto, Alexandria and
Naaman, Zohar and
Lee, I-Ta and
Pacheco, Maria Leonor",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1970/",
pages = "42544--42577",
ISBN = "979-8-89176-390-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Structured Clustering Approach for Inducing Media Narratives
%A Das, Rohan
%A Deshmukh, Advait
%A Leto, Alexandria
%A Naaman, Zohar
%A Lee, I-Ta
%A Pacheco, Maria Leonor
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F das-etal-2026-structured
%X 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.
%U https://aclanthology.org/2026.acl-long.1970/
%P 42544-42577
Markdown (Informal)
[A Structured Clustering Approach for Inducing Media Narratives](https://aclanthology.org/2026.acl-long.1970/) (Das et al., ACL 2026)
ACL
- Rohan Das, Advait Deshmukh, Alexandria Leto, Zohar Naaman, I-Ta Lee, and Maria Leonor Pacheco. 2026. A Structured Clustering Approach for Inducing Media Narratives. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 42544–42577, San Diego, California, United States. Association for Computational Linguistics.