Citation-based Plagiarism Detection: Practicability on a Large-Scale Scientific Corpus. Gipp, B., Meuschke, N., & Breitinger, C. Journal of the Association for Information Science and Technology, 65(8):1527–1540, August, 2014. ![Citation-based Plagiarism Detection: Practicability on a Large-Scale Scientific Corpus [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex The automated detection of plagiarism is an information retrieval task of increasing importance as the volume of readily accessible information on the web expands. A major shortcoming of current automated plagiarism detection approaches is their dependence on high character-based similarity. As a result, heavily disguised plagiarism forms, such as paraphrases, translated plagiarism, or structural and idea plagiarism, remain undetected. A recently proposed language-independent approach to plagiarism detection, Citation-based Plagiarism Detection (CbPD), allows the detection of semantic similarity even in the absence of text overlap by analyzing the citation placement in a document's full text to determine similarity. This article evaluates the performance of CbPD in detecting plagiarism with various degrees of disguise in a collection of 185,000 biomedical articles. We benchmark CbPD against two character-based detection approaches using a ground truth approximated in a user study. Our evaluation shows that the citation-based approach achieves superior ranking performance for heavily disguised plagiarism forms. Additionally, we demonstrate CbPD to be computationally more efficient than character-based approaches. Finally, upon combining the citation-based with the traditional character-based document similarity visualization methods in a hybrid detection prototype, we observe a reduction in the required user effort for document verification.
@article{GippMB14,
title = {Citation-based Plagiarism Detection: Practicability on a Large-Scale Scientific Corpus},
author = {Gipp, Bela and Meuschke, Norman and Breitinger, Corinna},
year = 2014,
month = aug,
journal = {Journal of the Association for Information Science and Technology},
volume = {65},
number = {8},
pages = {1527--1540},
issn = {2330-1635},
doi = {10.1002/asi.23228},
url = {https://gipplab.uni-goettingen.de/wp-content/papercite-data/pdf/gipp13b.pdf},
abstract = {The automated detection of plagiarism is an information retrieval task of increasing importance as the volume of readily accessible information on the web expands. A major shortcoming of current automated plagiarism detection approaches is their dependence on high character-based similarity. As a result, heavily disguised plagiarism forms, such as paraphrases, translated plagiarism, or structural and idea plagiarism, remain undetected. A recently proposed language-independent approach to plagiarism detection, Citation-based Plagiarism Detection (CbPD), allows the detection of semantic similarity even in the absence of text overlap by analyzing the citation placement in a document's full text to determine similarity. This article evaluates the performance of CbPD in detecting plagiarism with various degrees of disguise in a collection of 185,000 biomedical articles. We benchmark CbPD against two character-based detection approaches using a ground truth approximated in a user study. Our evaluation shows that the citation-based approach achieves superior ranking performance for heavily disguised plagiarism forms. Additionally, we demonstrate CbPD to be computationally more efficient than character-based approaches. Finally, upon combining the citation-based with the traditional character-based document similarity visualization methods in a hybrid detection prototype, we observe a reduction in the required user effort for document verification.},
keywords = {Plagiarism Detection},
annotation = {Venue Rating: SJR Q1},
file = {D:\Zotero\nmeuschke\Data\storage\EW4EB6II\GippMB14.pdf}
}
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