When Algorithms Guard Democracy: Measuring Authoritarian Rhetorical Behaviour in Political Speech

Democratic erosion often begins rhetorically before institutions show visible damage. Here we test whether large language models (LLMs) can detect early linguistic signals of authoritarian drift in political speech. Formal speeches by Adolf Hitler (1922–1939), Donald Trump (2017–2025), Nicola Sturgeon (2014–2023), Giorgia Meloni (2022–2025) and Viktor Orban (2022–2025) were scored using an 11-indicator taxonomy derived from the Levitsky–Ziblatt framework and evaluated independently by GPT-4o, Gemini 2.5-Pro and Grok-4-Fast, with near-perfect inter-model agreement. Principal Component Analysis revealed two poles: an authoritarian–populist cluster (Hitler–Trump–Orban) and a democratic-institutional pole (Meloni–Sturgeon). To quantify proximity to an authoritarian reference, we introduce the Authoritarian Reference Index (ARI), defined such that it captures both its alignment and intensity relative to the Hitler gold-standard vector. Trump exhibited the highest proximity to the reference (99.1% alignment, 80.7% intensity), followed by Orban, who mirrored the structural alignment (97.6%) with a moderated intensity (72.4%). In contrast, the democratic-institutional pole was distinguished by significantly lower intensity scores, with Meloni (16.4%) and Sturgeon (22.3%) remaining distant from the authoritarian magnitude despite varying degrees of structural overlap. These results show that extreme rhetorical peaks carry disproportionate diagnostic weight and that LLMs can expose structural authoritarian patterns relevant for democratic monitoring.