Enhancing Urdu Intrinsic Plagiarism Detection Through Stylometry Features and Machine Learning

The creation of digital content and the easy accessibility of information have led to a surge in academic and textual plagiarism. Plagiarism detection in multiple languages is essential to maintain the integrity of academic and literary works. In the context of the Urdu language, there is a growing need for effective plagiarism detection methods that are tailored to its unique linguistic characteristics. Existing Urdu plagiarism detection tools often rely on external sources or lack robustness in handling intrinsic forms of plagiarism, where the copied content is slightly modified or paraphrased. This research aims to bridge this gap by developing an intrinsic plagiarism detection system for the Urdu language, using a combination of machine learning, ensemble learning and Multi-Layer Perceptron (MLP). Furthermore, to train and evaluate our plagiarism detection models, we manually curate a corpus comprising a substantial collection of 1807 documents in Urdu. This corpus forms the foundation of our research, enabling us to develop and fine-tune our detection algorithms to effectively identify instances of intrinsic plagiarism in Urdu text. To comprehensively assess the unique stylistic fingerprints of documents, we employ a diverse set of word based stylometry features. This multifaceted approach enhances our ability to pinpoint instances of plagiarism in a robust manner. This research contributes to the ongoing efforts to combat plagiarism and uphold the integrity of written content, particularly in the context of the Urdu language, while also showcasing the effectiveness of different word based stylometry features in addressing this critical issue.