Demonstration of citation pattern analysis for plagiarism detection

@article{Gipp2013DemonstrationOC,
  title={Demonstration of citation pattern analysis for plagiarism detection},
  author={Bela Gipp and Norman Meuschke and Corinna Breitinger and Mario Lipinski and A. N{\"u}rnberger},
  journal={Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval},
  year={2013}
}
  • Bela Gipp, Norman Meuschke, +2 authors A. Nürnberger
  • Published 2013
  • Computer Science
  • Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Limitations of Plagiarism Detection Systems State-of-the-art plagiarism detection approaches capably identify copy & paste and to some extent slightly modified plagiarism. However, they cannot reliably identify strongly disguised plagiarism forms, including paraphrases, translated plagiarism, and idea plagiarism, which are forms of plagiarism more commonly found in scientific texts. This weakness of current systems results in a large fraction of today’s scientific plagiarism going undetected. 
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A report of how semantic similarity measures can be used in the plagiarism detection task is presented, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. Expand
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An overview of the best-known methods of detection of plagiarism that exist is given and the concept of big data is defined as one of these techniques that applied in the phase of extraction of documents sources for plagiarism detection. Expand
Comparing and combining Content‐ and Citation‐based approaches for plagiarism detection
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This work compares content and citation‐based approaches for plagiarism detection with the goal of evaluating whether they are complementary and if their combination can improve the quality of the detection and concluded that a combination of the methods can be beneficial. Expand
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It is shown how direct borrowings from scientific works can be identified by comparing bibliographies in view of their unique structure, which varies from one publication to another. Expand
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It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Expand
Citation pattern matching algorithms for citation-based plagiarism detection: greedy citation tiling, citation chunking and longest common citation sequence
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Three algorithms are introduced and it is shown that if these algorithms are combined, common forms of plagiarism can be detected reliably and Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence are combined. Expand
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PubMed Central
PubMed Central(PMC)是美国国立卫生研究院国立医学图书馆生物技术与信息中心开发和维护的生物医学与生命科学期刊文献免费数字文档库。其宗旨是承担起数字时代世界级图书馆的作用。它不是期刊出版商。出版商自愿加入PMC,并需满足一定的科研水平和编辑质量标准。
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Portal Plagiat -Softwaretest 2012 Online Source Retrieved November 27, 2012 from: http://plagiat .htw-berlin.de/collusion-test
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