Forms of Plagiarism in Digital Mathematical Libraries

Schubotz, Moritz; Teschke, Olaf; Stange, Vincent; Meuschke, Norman; Gipp, Béla

doi:10.1007/978-3-030-23250-4_18

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…We will increase the number of test cases and their degree of obfuscation to further support our results. For this purpose, we are collaborating with a major mathematical publishing service [38].…”

Section: Discussionmentioning

confidence: 99%

Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

Meuschke

Stange

Schubotz

et al. 2019

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional textbased detection approaches for academic literature in the STEM disciplines. The data and code of our study are openly available at https://purl.org/hybridPD

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Section: Discussionmentioning

confidence: 99%

Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

Meuschke

Stange

Schubotz

et al. 2019

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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“…with inhomogeneous text can lead to erroneous and even completely ridiculous conclusions. An exploratory analysis of different forms of plagiarism (explicit and disguised) observable in mathematical publications is presented in [21], where the authors investigated editorial notes from zbMATH and compared them to the results of current plagiarism detection systems. The investigation of the selected cases indicates that the application of the text-based iTh-system appears insufficient for analyzing similarities in mathematical publications.…”

Section: Ithenticate Systemmentioning

confidence: 99%

The similarity index of scientific publications with equations and formulas, identification of self-plagiarism, and testing of the iThenticate system

Polyanin,

Shingareva

2022

Preprint

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“…This kind of autocomplete and error-correction type-hinting system would be beneficial for various use-cases, e.g., in educational software or for search engines as a preprocessing step of the input. Plagiarism Detection Systems: As previously mentioned, plagiarism detection systems [29,39,41] would benefit from a system capable of distinguishing conventional from uncommon notations. The approaches described by Meuschke et al [39] outperform existing approaches by considering frequency distributions of single identifiers (expressions of complexity one).…”

Section: Applicationsmentioning

confidence: 99%

“…Fundamental knowledge on frequency distributions of math formulae is beneficial for numerous applications in MathIR, ranging from educational purposes [3] to math recommendation systems, search engines [22,25], and even automatic plagiarism detection systems [29,39,41]. For example, students can search for the conventions to write certain quantities in formulae; document preparation systems can integrate an auto-completion or auto-correction service for math inputs; search or recommendation engines can adjust their ranking scores with respect to standard notations; and plagiarism detection systems can estimate whether two identical formulae indicate potential plagiarism or are just using the conventional notations in a particular subject area.…”

Section: Introductionmentioning

confidence: 99%

Discovering Mathematical Objects of Interest—A Study of Mathematical Notations

Greiner-Petter

Schubotz

Müller

et al. 2020

Proceedings of the Web Conference 2020

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Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems.The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking P (α,β) n (x) with 'Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.

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Forms of Plagiarism in Digital Mathematical Libraries

Cited by 9 publications

References 26 publications

Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations

The similarity index of scientific publications with equations and formulas, identification of self-plagiarism, and testing of the iThenticate system

Discovering Mathematical Objects of Interest—A Study of Mathematical Notations

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