@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://preview.aclanthology.org/ingest-acl/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."
}