From threat to repair: metaphorical architecture and cognitive mobilization in Trumpist governing-coalition texts.

Authors

Xuhai Chen, Zhiheng Zheng, Jiaxuan Lv

Published in

Frontiers in psychology. Volume 17. Pages 1888538. Epub Aug 03, 2026.

Abstract

Research on Trumpism has concentrated on Donald Trump's campaign speeches and social media, while the long-form writings of governing-coalition elites remain underexamined. Book-length texts offer more complete argumentative structures than tweets or short speeches, providing a fuller view of how elites construct crisis narratives through metaphor.
Six book-length works published before their authors entered core positions in the Trump governing coalition were analyzed using an integrated framework combining BERTopic topic modeling, manual metaphor identification, source-target domain mapping, actor-positioning coding, and multi-label emotion appraisal within corpus-assisted critical metaphor analysis.
Across the six texts, 2,074 contextually verified linguistic metaphors were identified, concentrated in six focal source domains-war/conflict, decline/fall, corruption/disease, invasion/infiltration, betrayal/abandonment, and revival/reconstruction-forming a pronounced crisis-repair asymmetry: threat-oriented domains account for over 90% of instances, while revival/reconstruction provides a limited but emotionally concentrated repair script. Actor positioning reveals "us vs. internal them" as the predominant pattern, locating antagonists inside the political community. Emotion-appraisal profiles differentiate these domains: anger and pride/dignity characterize war/conflict, loss/nostalgia marks decline/fall, disgust and humiliation cluster in corruption/disease, and hope and sacredness define revival/reconstruction.
These findings indicate a stable, cross-issue crisis-and-repair metaphorical architecture that extends beyond a single leader's rhetoric, functioning as a shared cognitive mobilization structure at the textual level. The study contributes theoretically by showing how differentiated metaphor-emotion profiles organize crisis construction, blame attribution, and repair imagination in political texts. It also contributes methodologically by offering an operational pathway for integrating computational topic modeling with corpus-assisted critical metaphor analysis.

PMID:
42609533
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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