The study analyzes oral political language in U.S. presidential debates from 1960 to 2024, focusing on 19 candidates. It finds a clear trend toward simplification: sentence length and complex terms have decreased, while emotional tone has risen and logical, rational content has diminished. The research also explores whether specific presidents exhibit unique stylistic traits and whether language patterns correlate with electoral success.
The article investigates the vocabulary richness in oral political communication, focusing on how word usage varies across texts of different lengths. It proposes a model that explains lexicon growth by dividing the overall vocabulary into terms derived from general and specialized glossaries.
The study investigates how bilingual politicians structure the timing of their speeches in Luxembourgish and French, analyzing 400 sentences from ten speakers. Rhythm metrics were computed for consonants and vowels, revealing that consonant patterns are largely speaker-specific while vowel patterns are strongly influenced by language choice. French tokens exhibited longer, more variable vowels and vocalic intervals, whereas consonant timing differences were smaller, with no significant language-by-gender interactions.
The study examined 400 utterances from 10 politicians speaking in both Luxembourgish and French to determine how much of charismatic prosody is due to speaker identity versus language. Mixed‑effects modeling revealed that speaker identity explained most of the variance, while language contributed less but still produced systematic differences: French speech had higher shimmer and phrase‑final F0, suggesting a polite, respectful tone, whereas Luxembourgish speech showed stronger mid‑frequency spectral energy, indicating a more vocally present profile. These acoustic patterns reflect the sociolinguistic roles of Luxembourgish as an informal identity language and French as a high‑prestige institutional variety.
arXiv:2508.16013v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideologica...
arXiv:2608.30828v1 Announce Type: new Abstract: We present three large-scale studies of spoken parliamentary speech across four Slavic languages (Croatian, Czech, Polish, Serbian), drawing on over 6,...
arXiv:2609.15207v1 Announce Type: new Abstract: Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before ele...
The paper investigates the linguistic characteristics of ChatGPT-generated text, comparing it to 1,000 scientific publications and exploring its relation to concepts of ‘bullshit’ in political speech and workplace contexts. By applying hypothesis‑testing methods, the authors demonstrate that a statistical model of bullshit can link the artificial bullshit produced by ChatGPT to the political and workplace functions of bullshit observed in natural human language.
The study examines how the use of evidence-oriented versus intuition-oriented language—measured by the Evidence‑Minus‑Intuition (EMI) score—varies among individual U.S. Congress members and relates to their legislative effectiveness. It finds that more ideologically extreme legislators tend to use less evidence-oriented language on the floor, that EMI scores are consistent across floor speeches and Twitter posts (though lower on Twitter overall), and that higher EMI scores on the floor predict greater legislative effectiveness even after controlling for ideology and other factors. The research highlights evidence‑based communication as a significant individual attribute linked to legislative success.
arXiv:2606. 28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space.
arXiv:2608. 03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and therefore cannot determine whether they evolve together across languages.
Filled pauses (FPs) are a universal feature of spontaneous speech, yet most studies rely on small, single-language corpora, limiting the generalisability of their findings. We analyse ~4,000 hours of parliamentary speech across four related Slavic languages (Croatian, Czech, Polish, Serbian).
A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epanorthosis, the self-correction of the specimen «This is not a course. It is a journey of transformation».