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    • Abbreviated Title: Int. J. Lang. Lit. Linguist.
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    • DOI: 10.18178/IJLLL
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HOME > Archives > 2025 > Volume 11 Number 6 (2025) >
IJLLL 2025 Vol.11(6): 280-289
DOI: 10.18178/IJLLL.2025.11.6.616

A Corpus-based Analysis of American Bipartisanship Discourse on Immigrants from 2016–2023

Liu Xiuzhi* and Xu Jing
School of Political Science and Public Administration (National Security School), Urumqi City, Xinjiang Uygur Autonomous Region, 830046, China
Email: 3375973592@qq.com (L.X.Z.)
*Corresponding author

Manuscript received August 4, 2025; accepted November 14, 2025; published December 18, 2025.

Abstract—Immigration is an important issue in the political game between the two parties in the United States, and it is also a basic variable affecting the national governance and political development of the United States. This paper uses the single corpus Antconc method to comb the immigration-related discourses in news articles and policy documents supporting the respective websites of the two US political parties from 2016 to 2023 to build a corpus. On the basis of combing and analyzing the high-frequency substantive words, keywords, adjective collocation and modal verb collocation in the two parties’ immigration discourse in the corpus, it is found that there are triple tension disputes between the two parties in the United States on the immigration policy, including values, economic interests and partisan politics. It is the different positions of the two parties on the immigration issue that aggravate the debate on the immigration issue, making the immigration policy in the United States gradually become polarized, and then lead to the politicization of identity politics and veto.

Keywords—democratic party, republican party, immigration discourse, polarization

Cite: Liu Xiuzhi and Xu Jing, "A Corpus-based Analysis of American Bipartisanship Discourse on Immigrants from 2016–2023," International Journal of Languages, Literature and Linguistics, vol. 11, no. 6, pp. 280-289, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

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