Plagiarism types and detection methods: a systematic survey of algorithms in text analysis
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 disti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1504725/full |
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| author | Altynbek Amirzhanov Cemil Turan Alfira Makhmutova |
| author_facet | Altynbek Amirzhanov Cemil Turan Alfira Makhmutova |
| author_sort | Altynbek Amirzhanov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4efe70dce1e7489fbbbe733487d4ae4e |
| institution | DOAJ |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-4efe70dce1e7489fbbbe733487d4ae4e2025-08-20T02:55:53ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-03-01710.3389/fcomp.2025.15047251504725Plagiarism types and detection methods: a systematic survey of algorithms in text analysisAltynbek Amirzhanov0Cemil Turan1Alfira Makhmutova2Computer Science, SDU University, Kaskelen, KazakhstanComputer Science, SDU University, Kaskelen, KazakhstanGeneral Education, New Uzbekistan University, Tashkent, UzbekistanPlagiarism 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.https://www.frontiersin.org/articles/10.3389/fcomp.2025.1504725/fullplagiarism detectiontext analysisnatural language processingplagiarism typesmachine learningAI-generated content |
| spellingShingle | Altynbek Amirzhanov Cemil Turan Alfira Makhmutova Plagiarism types and detection methods: a systematic survey of algorithms in text analysis Frontiers in Computer Science plagiarism detection text analysis natural language processing plagiarism types machine learning AI-generated content |
| title | Plagiarism types and detection methods: a systematic survey of algorithms in text analysis |
| title_full | Plagiarism types and detection methods: a systematic survey of algorithms in text analysis |
| title_fullStr | Plagiarism types and detection methods: a systematic survey of algorithms in text analysis |
| title_full_unstemmed | Plagiarism types and detection methods: a systematic survey of algorithms in text analysis |
| title_short | Plagiarism types and detection methods: a systematic survey of algorithms in text analysis |
| title_sort | plagiarism types and detection methods a systematic survey of algorithms in text analysis |
| topic | plagiarism detection text analysis natural language processing plagiarism types machine learning AI-generated content |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1504725/full |
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