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Decoding AI authorship: can LLMs truly mimic human style across literature and politics?

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Title: Decoding AI authorship: can LLMs truly mimic human style across literature and politics?
Authors: Alsadhan, Nasser A1 (AUTHOR)
Source: Digital Scholarship in the Humanities. Jun2026, Vol. 41 Issue 2, p594-612. 19p.
Subjects: Attribution of authorship, Stylometry, Language models, Politicians, Obama, Barack, 1961-, Content analysis, Classification algorithms, Whitman, Walt, 1819-1892, Literary style, Trump, Donald, 1946-, Artificial intelligence, Wordsworth, William, 1770-1850
Abstract: Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Barack Obama. Utilizing a zero-shot prompting framework with strict thematic alignment, we generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and feature-based machine learning (XGBoost). Our methodology integrates Linguistic Inquiry and Word Count (LIWC) markers, perplexity, and readability indices to assess divergence between AI-generated and human-authored text. Results demonstrate that AI-generated mimicry remains highly detectable, with XGBoost models trained on a restricted set of eight stylometric features achieving accuracy comparable to high-dimensional neural classifiers. Post-hoc feature importance analysis indicates that perplexity is the most influential discriminative metric, suggesting systematic differences in the distributional regularity of AI outputs relative to the greater variability observed in human writing. While LLMs exhibit distributional convergence with human authors on low-dimensional heuristic features, such as syntactic complexity and readability, they do not yet fully replicate the nuanced affective density and stylistic variance inherent in the human-authored corpus. By isolating measurable statistical divergences in current generative mimicry, this study provides a structured benchmark for LLM stylistic behavior and offers insights for authorship attribution in digital humanities and social media contexts. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the authorial signatures of prominent literary and political figures: Walt Whitman, William Wordsworth, Donald Trump, and Barack Obama. Utilizing a zero-shot prompting framework with strict thematic alignment, we generated synthetic corpora evaluated through a complementary framework combining transformer-based classification (BERT) and feature-based machine learning (XGBoost). Our methodology integrates Linguistic Inquiry and Word Count (LIWC) markers, perplexity, and readability indices to assess divergence between AI-generated and human-authored text. Results demonstrate that AI-generated mimicry remains highly detectable, with XGBoost models trained on a restricted set of eight stylometric features achieving accuracy comparable to high-dimensional neural classifiers. Post-hoc feature importance analysis indicates that perplexity is the most influential discriminative metric, suggesting systematic differences in the distributional regularity of AI outputs relative to the greater variability observed in human writing. While LLMs exhibit distributional convergence with human authors on low-dimensional heuristic features, such as syntactic complexity and readability, they do not yet fully replicate the nuanced affective density and stylistic variance inherent in the human-authored corpus. By isolating measurable statistical divergences in current generative mimicry, this study provides a structured benchmark for LLM stylistic behavior and offers insights for authorship attribution in digital humanities and social media contexts. [ABSTRACT FROM AUTHOR]
ISSN:2055768X
DOI:10.1093/llc/fqag040