Abstract
This chapter presents Math-based Plagiarism Detection (MathPD)—a new approach we proposed to improve the detection of academic plagiarism, primarily in the science, technology, engineering, and mathematics (STEM) fields.
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Notes
- 1.
The labels ci, co, and cn reflect the elements that denote identifiers, numbers, and operators in Content MathML markup. Appendix C in the electronis supplementary material presents the essentials of the MathML standard.
- 2.
Higgins et al. used the PDS iThenticate to check submissions to a medical journal for plagiarism. They found that a similarity score of 15% achieved the best tradeoff between sensitivity and specificity for classifying manuscripts as plagiarized or original [227, p. 3]. Other, anecdotal reports support this finding. The question which percentage of similarity in publications is generally treaded as acceptable received 261 replies on the social networking site ResearchGate by June 2020. Most of the replies suggested percentages were in the range of 10%–20% [389].
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Meuschke, N. (2023). Math-based Plagiarism Detection. In: Analyzing Non-Textual Content Elements to Detect Academic Plagiarism. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-42062-8_5
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DOI: https://doi.org/10.1007/978-3-658-42062-8_5
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