Plagiarism - A Survey

By Hermann A. Maurer, F. Kappe, Bilal Zaka. Journal of universal computer science (Online), 2006

398 citations recorded by the source corpus.

Plagiarism in the sense of "theft of intellectual property" has been around for as long as humans have produced work of art and research. However, easy access to the Web, large databases, and telecommunication in general, has turned plagiarism into a serious problem for publishers, researchers and educational institutions. In this paper, we concentrate on textual plagiarism (as opposed to plagiarism in music, paintings, pictures, maps, technical drawings, etc.). We first discuss the complex general setting, then report on some results of plagiarism detection software and finally draw attention to the fact that any serious investigation in plagiarism turns up rather unexpected side-effects. We believe that this paper is of value to all researchers, educators and students and should be considered as seminal work that hopefully will encourage many still deeper investigations.

Subfields: Safety Research

View Paper: https://www.semanticscholar.org/paper/209302b33726db59303707186707bd080e68fe59

How researchers see this paper

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In Safety Research

1. Dawn Song
2. Neil Zhenqiang Gong
3. Martin Potthast
4. Arvind Narayanan
5. Sunipa Dev
6. Adina Williams

Citations in the corpus

• An Evaluation Framework for Plagiarism Detection (2010, background): “There are a few surveys about automatic plagia-rism detection in text [7, 8, 14] and in code [12, 17, 19, 20].”
• Collaboration Versus Cheating: Reducing Code Plagiarism in an Online MS Computer Science Program (2018, background): “lis, MN, USA T. Mason et al. information. Our hope is this will further reduce our plagiarism rate. 5 PRIOR WORK The basic concepts around plagiarism are hardly new and have been extensively reviewed [27, 28, 37]. Indeed, the literature contains clear discussions on this problem with respect to prog”
• On the Feasibility of Internet-Scale Author Identification (2012, background): “Plagiarism detection can be seen as complementary to stylometric authorship attribution: it attempts to detect common content between documents, even if the style may have been changed [37].”