Déjà vu—A study of duplicate citations in Medline

Errami, Mounir; Hicks, Justin M.; Fisher, Wayne G.; Trusty, David; Wren, Jonathan D.; Long, Tara C.; Garner, Harold R.

doi:10.1093/bioinformatics/btm574

Cited by 87 publications

(56 citation statements)

References 20 publications

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“…Two texts, and , are more likely to be similar if they share a larger percentage of their references. This assumption is supported by results of text-based PD studies [10].…”

Section: Relative Numbermentioning

confidence: 56%

Citation pattern matching algorithms for citation-based plagiarism detection

Gipp

Meuschke

2011

Proceedings of the 11th ACM Symposium on Document Engineering

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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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“…Two texts, and , are more likely to be similar if they share a larger percentage of their references. This assumption is supported by results of text-based PD studies [10].…”

Section: Relative Numbermentioning

confidence: 56%

Citation pattern matching algorithms for citation-based plagiarism detection

Gipp

Meuschke

2011

Proceedings of the 11th ACM Symposium on Document Engineering

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“…Editorial responses 16 Baserga, 2011;Chalmers, 2009;Joob & Wiwanitkit, 2013Keeble, 2016;Klosterman, 2013;Mohapatra & Samal, 2014;Teixeira da Silva, 2016;White, 2011;Wiwanitkit, 2012Wiwanitkit, , 2014Wiwanitkit, , 2015Zulqifar, 2007. Secondary research 12 Amos, 2014;Cheung & Driver, 2004;Elbeck, 2009;Errami et al 2007;Halupa, 2014;Horbach & Halffman, 2017;Ison, 2012Ison, , 2015Kokol et al 2016;Roig, 2005;Schein & Paladugu, 2001;Sun & Yang, 2015. Primary research 8 Bretag & Carapiet, 2007;Halupa & Bolliger, 2013Halupa, Breitenbach, & Anast, 2016;Jones, 2011;Mkhize et al, 2009;Roig & Ballew, 1994;Vincent Robinson, 2016.…”

Section: Analysis Of Typologies Of Evidenceunclassified

Self-Plagiarism Research Literature in the Social Sciences: A Scoping Review

Eaton

Crossman

2018

Interchange

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Self-plagiarism is a contentious issue in higher education, research and scholarly publishing contexts. The practice is problematic because it disrupts scientific publishing by overemphasizing results, increasing journal publication costs, and artificially inflating journal impact, among other consequences. We hypothesized that there was a dearth of empirical studies on the topic of self-plagiarism, with an over-abundance of editorial and commentary articles based on anecdotal evidence. The research question was: What typologies of evidence characterize the literature on self-plagiarism in scholarly and research journals? We conducted a scoping review, using the search terms "self-plagiarism" and "self-plagiarism" (hyphenated), consulting five social sciences research databases, supplemented by a manual search for articles, resulting in over 5900 results. After removing duplicates and excluding non-scholarly sources, we arrived at a data set of 133 sources, with publication dates ranging from 1968 to 2017. With an interrater reliability of over 93% between two researchers, our typological analysis revealed 47 sources (34.3%) were editorials; 41 (29.9%) were conceptual research (including teaching cases); 16 (11.7%) were editorial responses; 12 (8.6%) were secondary research; and only 8 sources (5.8%) were primary research. There is little guidance in the available literature to graduate students or their professors about how to disentangle the complexities of self-plagiarism. With primary and secondary research combined accounting for 14.4% of overall contributions to the data set, and primary research constituting only 6% of overall contributions, we conclude with a call for more empirical evidence on the topic to support contributions to the scholarly dialogue.

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“…Various software/web-servers/databases have been widely used to detect self-plagiarism (e.g., Déjà vu (Errami et al, 2008); eTBLAST (Errami, Wren, Hicks, and Garner, 2007); Cross-Check (http://www.crossref.org/crosscheck/ index.html); WCopyfind (http://plagiarism.bloomfieldme dia.com/z-wordpress/software/). It is recommended that all researchers make use of such resources to check for evidence of self-plagiarism before submitting their manuscripts to a scientific journal for publication.…”

Section: Prevention Of Self Plagiarismmentioning

confidence: 99%