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Academic Plagiarism Detection: A Systematic Literature Review

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Foltýnek, Tomáš, Meuschke, Norman, Gipp, Bela·2020·ACM Computing Surveys
DOI 10.1145/3345317
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Abstract

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

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  • Plagiarism detection has seen significant advancement with increased use of AI techniques, specifically semantic text analysis and machine learning algorithms.

    “A recent systematic literature review (Foltýnek, Meuschke, & Gipp, 2019) showed significant advancement in plagiarism detection with the increased use of AI techniques -specifically, semantic text analysis methods (e.g., latent semantic analysis and word embeddings) and machine learning algorithms.”

    From Assessment in the age of artificial intelligence (2022) · cites this paper as Foltýnek, Meuschke, & Gipp, 2019

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Authors
Foltýnek, T., Meuschke, N., & Gipp, B.
Affiliations
  • University of Wuppertal (DE)
  • University of Wuppertal, Germany and University of Konstanz, Germany
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2019-10-16
Volume
52
Issue
6
Pages
1-42
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3345317
OpenAlex
W2980420460
Cited by
238
References
163
Citations by year
2026: 42 · 2025: 37 · 2024: 34 · 2023: 39 · 2022: 34 · 2021: 25 · 2020: 22 · 2019: 5
FWCI
38.00
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion, other-oa)
  • KOPS (University of Konstanz) (submittedVersion, cc-by-sa)
  • GRO (submittedVersion, cc-by)
Funders
European Social Fund (CZ.02.2.69/0.0/0.0/16_027/0007953)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
plagiarism avoidance, semantic text analysis

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