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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 26, No. 0, pp.930-946
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Aug 2026
Received 29 May 2026 Revised 18 Jul 2026 Accepted 18 Jul 2026
DOI: https://doi.org/10.15738/kjell.26..202608.930

A Corpus-Based Study of War Metaphor Variation in U.S. Presidential Campaign Speeches: Gender, Party Affiliation, and Political Seniority (2016–2024)

Qingxiang Cao ; Yaochen Deng ; Hyun-Jong Hahm ; Se-Eun Jhang
(First author) PhD Student, Center for Northeast Asian Studies, Dalian University of Foreign Languages 1670491216@qq.com
(Co-author) Professor, Center for Northeast Asian Studies, Dalian University of Foreign Languages dengyaochen@gmail.com
(Co-author) Associate Professor, Division of English and Applied Linguistics University of Guam hhahm@triton.uog.edu
(Corresponding author) Professor, Department of English Language and Literature Korea Maritime and Ocean University, Tel: +82-51-410-4590 jhang@kmou.ac.kr


© 2026 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This corpus-based study examines the identification, classification, and distribution of war metaphors in a 241,440-word corpus of U.S. presidential campaign speeches from the 2016, 2020, and 2024 election cycles. It investigates variation across three extralinguistic variables: gender, party affiliation, and political seniority. FrameBERT was used for automatic token-level metaphor detection, followed by manual contextual validation and target-domain annotation. War-related metaphorical expressions were classified into eleven target-domain categories, and normalized frequencies were compared across groups. Omnibus chi-square tests indicated significant associations between the overall distribution of target domains and each of the three extralinguistic variables. At the descriptive level, female candidates showed higher normalized frequencies in the moral/value, nation, and other domains, whereas male candidates showed higher normalized frequencies in the institution and society domains. Democratic candidates showed higher normalized frequencies in the events/actions, moral/value, and other domains, while communication and institution metaphors occurred only in the Republican subset. Across political-seniority groups, high-seniority candidates showed the highest normalized frequency in the moral/value domain, whereas middle-seniority candidates showed the highest normalized frequency in the nation domain. These findings suggest that war-metaphor framing is associated with speakers’ social and political positioning in the present corpus. However, corpus-size imbalance, overlap among the explanatory variables, and the inclusion of repeated candidates across election cycles prevent the independent and interactive effects of the variables from being statistically distinguished. The study contributes to research on political metaphor variation and demonstrates the value of combining computational metaphor detection with manual contextual validation and target-domain analysis.

Keywords:

war metaphor, conceptual metaphor, political discourse, U.S. presidential campaign, metaphor variation

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