Citation pattern matching algorithms for citation-based plagiarism detection

Abstract

Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased, translated or idea plagiarism. The authors of this paper demonstrated in recent studies [4,15] that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of Citation-based Plagiarism Detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that common forms of plagiarism can be detected reliably if these algorithms are combined.

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“…Citation-based plagiarism detection (CbPD) proposed by Gipp et al [91] analyses patterns of in-text citations in academic documents, i.e., identical citations occurring in proximity or in a similar order within two documents. The idea is that in-text citations encode semantic information language-independently.…”

“…We attribute this development to the technical maturity of text extraction approaches. For plagiarism detection approaches that analyze non-textual content elements, e.g., academic citations and references [90,91,161,191], images [162], and mathematical content [163,165], document format conversion, and information extraction still present significant challenges.…”

Forms of Plagiarism in Digital Mathematical Libraries

“…To complement the many text analysis approaches and to improve the detection capabilities of concealed forms of academic plagiarism, researchers proposed approaches that analyze nontextual content features, such as academic citations [9,8,7,11,10,16,23] and images [18]. Nontextual content features in academic documents are a valuable source of semantic information that are largely independent of natural language text.…”

Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection

“…Limited works have been reported in this direction, where citation and structural analyses were focused or integrated with text-based analysis. A citation-based plagiarism detection method, which explored the potentials of citation-based analysis was reported, which used concepts such as bibliographic coupling, co-citation, and citation proximity analysis from the documents (Gipp & Meuschke, 2011;Meuschke, Gipp, & Breitinger, 2012;Gipp, Meuschke, Breitinger, Lipinski, & Nuernberger, 2013;Pertile, Rosso, & Moreira, 2013). The main idea behind these works is that plagiarized documents usually share common citation patterns.…”