@inproceedings{cho-etal-2026-undocumented,
    title = "``Undocumented Immigrants'' != ``Illegal Aliens'': Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms",
    author = "Cho, Yejin  and
      Chronis, Gabriella  and
      Sudarsanam, Nitin  and
      Barcenas-Martinez, Kevin  and
      Erk, Katrin",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.23/",
    pages = "348--365",
    ISBN = "979-8-89176-413-2",
    abstract = "Do politically charged terms with similar referents, like ``undocumented immigrants'' (UI) ``illegal aliens'' (IA) differ only in who uses them, or also in what they mean? We investigate usage patterns by projecting contextual embeddings into interpretable psycholinguistic feature space, and extracting narrative scenes with LLMs. We find that in partisan news, the term IA appears in contexts emphasizing causation and fear. UI appears in contexts emphasizing consequences experienced and shared humanity. Scene abstraction reveals parallel patterns: IA is embedded in narratives of criminality and threat, UI in narratives of vulnerability and governance. Beyond indexing speaker identity, these terms impart different construals on migrants: as agents of harm versus patients of circumstance. This dual-track methodology adds new tools to the growing body of computational approaches for understanding the conceptual framing of politically charged topics."
}