Evolution and Analysis of Modern Plagiarism Detection Methods: A Systematic Review
DOI:
https://doi.org/10.48001/978-81-980647-5-2-10Keywords:
String-Based Detection, Semantic Analysis, Machine learning, Plagiarism DetectionAbstract
This systematic review examines the advancement and effectiveness of plagiarism detection methodologies in academic and professional contexts from 2000 to 2024. Through comprehensive analysis of 87 research papers and technical implementations, we evaluate three primary approaches: string-based detection, semantic analysis, and machine learning integration. Our research demonstrates a significant evolution from basic pattern matching to sophisticated neural network-based systems, with modern methods achieving detection accuracy rates up to 98\%. The study reveals that while machine learning approaches show superior performance in complex cases, traditional methods maintain relevance for specific applications. This review contributes to the field by providing a detailed comparative analysis of detection methodologies and identifying critical areas for future development.
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References
Burrows, S., Tahaghoghi, S. M., & Zobel, J. (2007). Efficient plagiarism detection for large code repositories. Software - Practice and Experience, 37(2), 151–175. https://doi.org/10.1002/spe.750
Chowdhury, H. A., & Bhattacharyya, D. K. (2018). Plagiarism: Taxonomy, Tools, and Detection Techniques. Retrieved from http://arxiv.org/abs/1801.06323
Foltýnek, T., Meuschke, N., & Gipp, B. (2019). Academic plagiarism detection: A systematic literature review. ACM Computing Surveys, 52(6). https://doi.org/10.1145/3345317
Hambi, E. M., & Benabbou, F. (2020). A new online plagiarism detection system based on deep learning. International Journal of Advanced Computer Science and Applications, 11(9), 470–478. https://doi.org/10.14569/IJACSA.2020.0110956
Lancaster, T., & Culwin, F. (2001). Towards an error-free plagiarism detection process. Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, 57–60. https://doi.org/10.1145/507758.377473
Raparthi, M., Dodda, S. B., Reddy, S., Reddy, B., Thuniki, P., Maruthi, S., & Ravichandran, P. (2021). Advancements in natural language processing - A comprehensive review of AI techniques. Journal of Bioinformatics and Artificial Intelligence, 1(1), 1–10. https://biotechjournal.org/index.php/jbai/article/view/10
Weber, D. (2019). Plagiarism detectors are a crutch, and a problem. Nature, 567, 435.
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- 2024-11-30 (2)
- 2024-11-28 (1)
