Overview

Stylometry studies recurring features of language and other creative work to compare styles, investigate authorship, and understand variation across writers, genres, and settings. Methods range from character and word n-grams, lexical and syntactic features, and statistical distance measures to embeddings and large language models; they support attribution, verification, AI-text analysis, and forensic comparison. Simple, interpretable features often remain competitive, while newer approaches combine them with learned representations and test whether results transfer across languages, domains, and unseen authors. A central tension is that style signals can be useful yet confounded by genre, topic, editing, or shared training, and the same methods that identify authors can threaten privacy or be evaded by rewriting.

Latest papers

CardsList

35 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

  1. Narrative Fingerprints: Multi-Scale Author Identification via Novelty Curve Dynamics

    Apr 1, 2026Fred Zimmerman, Hilmar AIAuthorship AttributionStylometry

  2. Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?

    Mar 24, 2026Nasser A AlsadhanLanguage Model Generation EvaluationAI-Generated Text Detection

  3. Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent

    Feb 26, 2026Boyang Zhang, Yang ZhangPrivacy Leakage in Language ModelsAuthorship Attribution

  4. CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation

    Feb 13, 2026Yiran Rex Ma, Yuxiao Ye, Huiyuan XieAutomated EvaluationLegal NLP

  5. One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification

    Oct 15, 2025Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín +1StylometryAuthorship Attribution