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

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.