@inproceedings{sat-etal-2025-modelling,
    title = "Modelling the Relative Contributions of Stylistic Features in Forensic Authorship Attribution",
    author = "Sat, G. {\c{C}}a{\u{g}}atay  and
      Blake, John  and
      Pyshkin, Evgeny",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.123/",
    pages = "1066--1073",
    abstract = "This paper explores the extent to which stylistic features contribute to the task of authorship attribution in forensic contexts. Drawing on a filtered subset of the Enron email corpus, the study operationalizes stylistic indicators across four groups: lexical, syntactic, orthographic, and discoursal. Using R Programming Language for feature engineering and logistic regression modelling, we systematically assessed both the individual and interactive effects of these features on attribution accuracy. Results show that n-gram similarity consistently outperformed all other features, with the combined model of n-gram similarity and its interaction with other features achieving accuracy, precision and F1 scores of 91.6{\%}, 93.3{\%} and 91.7{\%} respectively. The model was subsequently evaluated on a subset of the TEL corpus to assess its applicability in a forensic setting. The findings highlight the dominant role of lexical similarity and suggest that integrating interaction effects can yield further performance gains in forensic authorship analysis."
}