Evolution and Analysis of Modern Plagiarism Detection Methods: A Systematic Review

Abstract

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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