@inproceedings{anabtawi-2026-stylometric,
    title = "A Stylometric and Statistical Pipeline for {U}rdu {AI}-Generated Text Classification",
    author = "Anabtawi, saeed A.",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.58/",
    pages = "472--475",
    abstract = "The proliferation of Large Language Models (LLMs) has introduced significant challenges regarding algorithmic bias, privacy, and the authenticity of digital content. While detection mechanisms for English are maturing, low-resource languages like Urdu{---}spoken by over 100 million people{---}require dedicated research. In this paper, we present a technical framework for Urdu AI-generated text detection developed for the *ACL shared task. We propose a hybrid pipeline that combines TF-IDF Character N-grams with a custom stylometric feature extractor designed to capture unique Urdu linguistic markers, including repeated word ratios, punctuation density, and formal function markers. Using a Linear Support Vector Machine (SVM) optimized via Stochastic Gradient Descent (SGD), our system achieves a balanced accuracy and $F_1$-score of 87.80{\%} on a dataset of 6,800 records. Our results demonstrate that a computationally efficient, classical machine learning approach{---}prioritizing stylistic signals over heavy preprocessing{---}remains highly effective for distinguishing between human-written and AI-generated Urdu text."
}