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        <title>“Undocumented Immigrants” != “Illegal Aliens”: Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T “Undocumented Immigrants” != “Illegal Aliens”: Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms
%A Cho, Yejin
%A Chronis, Gabriella
%A Sudarsanam, Nitin
%A Barcenas-Martinez, Kevin
%A Erk, Katrin
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F cho-etal-2026-undocumented
%X 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.
%U https://aclanthology.org/2026.starsem-conference.23/
%P 348-365

Markdown (Informal)

["Undocumented Immigrants" != "Illegal Aliens": Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms](https://aclanthology.org/2026.starsem-conference.23/) (Cho et al., *SEM 2026)

• “Undocumented Immigrants” != “Illegal Aliens”: Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms (Cho et al., *SEM 2026)

ACL

• Yejin Cho, Gabriella Chronis, Nitin Sudarsanam, Kevin Barcenas-Martinez, and Katrin Erk. 2026. "Undocumented Immigrants" != "Illegal Aliens": Decomposing the Conceptual and Narrative Landscapes of Partisan Immigration Terms. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 348–365, San Diego, California, United States. Association for Computational Linguistics.