Plagiarism types and detection methods: a systematic survey of algorithms in text analysis
Amirzhanov A. Turan C. Makhmutova A.
2025Frontiers Media SA
Frontiers in Computer Science
2025#7
Plagiarism in academic and creative writing continues to be a significant challenge, driven by the exponential growth of digital content. This paper presents a systematic survey of various types of plagiarism and the detection algorithms employed in text analysis. We categorize plagiarism into distinct types, including verbatim, paraphrasing, translation, and idea-based plagiarism, discussing the nuances that make detection complex. This survey critically evaluates existing literature, contrasting traditional methods like string-matching with advanced machine learning, natural language processing, and deep learning approaches. We highlight notable works focusing on cross-language plagiarism detection, source code plagiarism, and intrinsic detection techniques, identifying their contributions and limitations. Additionally, this paper explores emerging challenges such as detecting cross-language plagiarism and AI-generated content. By synthesizing the current landscape and emphasizing recent advancements, we aim to guide future research directions and enhance the robustness of plagiarism detection systems across various domains. Copyright
AI-generated content , machine learning , natural language processing , plagiarism detection , plagiarism types , text analysis
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Computer Science, SDU University, Kaskelen, Kazakhstan
General Education, New Uzbekistan University, Tashkent, Uzbekistan
Computer Science
General Education
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026