State-of-the-art in detecting academic plagiarism
Open accessAbstract
The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in this article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically detecting more strongly disguised plagiarism, such as paraphrases, translations or idea plagiarism, is the focus of current research. Proposed approaches for this task include intrinsic, cross-lingual and citation-based plagiarism detection. Each method offers unique strengths and weaknesses; however, none is currently mature enough for practical use. In the future, plagiarism detection systems may benefit from combining traditional character-based detection methods with these emerging detection approaches.
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Some of these tricks were highlighted as technical weaknesses, decreasing the detection accuracy in TMSPs.
“Some of these tricks were highlighted as technical weaknesses, decreasing the detection accuracy in TMSPs (Meuschke & Gipp, 2013).”
From Some students' plagiarism tricks, and tips for effective check (2021) · cites this paper as Meuschke & Gipp, 2013
Some students find ways to trick TMSPs.
“some students found ways to trick them.”
From Some students' plagiarism tricks, and tips for effective check (2021) · cites this paper as Meuschke & Gipp, 2013
Students can fool TMSPs by different acts that hamper them from identifying the text correctly by intentionally hiding the copied text, stated as 'Disguised Plagiarism'.
“Students can fool the TMSPs by different acts that hamper them from identifying the text correctly by intentionally hiding the copied text, stated as”
From Some students' plagiarism tricks, and tips for effective check (2021) · cites this paper as Meuschke & Gipp, 2013
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- Authors
- Meuschke, N., & Gipp, B.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Meuschke, Norman
- Venue
- International Journal for Educational Integrity
- Published
- 2013-06-06
- Volume
- 9
- Issue
- 1
- ISSN
- 1833-2595 (ISSN-L)
- OpenAlex
- W2096194908
- Cited by
- 112
- References
- 91
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 5 · 2023: 16 · 2022: 4 · 2021: 14 · 2020: 1 · 2019: 15 · 2018: 9 · 2017: 22 · 2016: 7 · 2015: 5 · 2014: 6 · 2013: 2
- FWCI
- 17.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal for Educational Integrity (publishedVersion)
- KOPS (University of Konstanz) (submittedVersion)
- GRO (submittedVersion)
- 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
- academic plagiarism, plagiarism detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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