Plagiarism Detection Software: Promises, Pitfalls, and Practices

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An increasing number of students at universities around the world seem to be submitting plagiarized texts to their instructors for credit, although no exact figures are available, either for how much was plagiarized in the past or how much is plagiarized now. Instructors, overwhelmed with an ever-increasing workload, wish for a simple method – rather like a litmus test – to quickly sort out the plagiarized works, so that they can concentrate their efforts on the rest of the students.

The good news is that software can be used to identify some text parallels that could constitute plagiarism. The bad news is that the reports are often not easy to interpret correctly, software can flag correctly referenced material as non-original content, and there are cases in which systems report no problems at all for heavily plagiarized texts, as studies conducted by the author in 2004, 2007, 2008, 2010, 2011, 2012 and 2013 have repeatedly shown. Different systems have also been shown in these studies to report various amounts of plagiarism for identical texts, as they use individual, often proprietary, algorithms and sometimes only examine samples of the text under investigation. This chapter will examine the promises and pitfalls of technology-based plagiarism detection and look at some good practices for using such software in a university setting.


Learn Management System Academic Integrity Spelling Error Academic Misconduct Text Parallel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Meyer zu Eissen, S., & Stein, B. (2006). Intrinsic plagiarism detection. In M. Lalmas et al. (Ed.), Presented at the ECIR 2006 (LNCS 3936, pp. 565–569). London: Springer. Available at Cited 17 Apr 2015.
  2. Fishman, T. (2009). “We know it when we see it” is not good enough: toward a standard definition of plagiarism that transcends theft, fraud, and copyright. In Proceedings of the Fourth Asia Pacific Conference on Educational Integrity (4APCEI) 28–30 September, University of Wollongong, NSW, Australia. Available at Cited 17 Apr 2015.
  3. Flominator. (n.d.). WikiBlame. [Web page]. Available at Cited 17 Apr 2015.
  4. Gipp, B. (2014). Citation-based plagiarism detection: Detecting disguised and cross-language plagiarism using citation pattern analysis. Berlin: Springer Vieweg.Google Scholar
  5. Grune, D., & Huntjens, M. (1989). Het detecteren van kopieën bij informatica-practica. In Informatie, 31(11), 864–867. English translation available at and the program code at Cited 17 Apr 2015.
  6. PAN 2015. (2015). Plagiarism detection, author identification, author profiling. [Yearly Competition at the University of Weimar]. Available at Cited 12 Apr 2015.
  7. Pecorari, D. (2013). Teaching to avoid plagiarism: How to promote good source use. Maidenhead: Open University Press.Google Scholar
  8. Pecorari, D., & Petrić, B. (2014). Plagiarism in second-language writing. In Language Teaching, 47, 269–302.Google Scholar
  9. PicaPica. (n.d.). Compare a text to Wikipedia. Available at Cited 17 Apr 2015.
  10. ScanMyEssay. (n.d.). How does viper use my essay/dissertation? [Web page]. Available at Cited 4 Apr 2015.
  11. Sutherland-Smith, W. (2013). Crossing the line: Collusion or collaboration in university group work? In Australian Universities Review, 55(1), 51–58.Google Scholar
  12. TinEye. (n.d.). Reverse image search. Available at Cited 17 Apr 2015.
  13. Turnitin. (2011). Plagiarism and the web: Myths and realities. An analytical study on where students find unoriginal content on the internet. [White paper]. Available at Cited 12 Apr 2015.
  14. VroniPlagWiki. (n.d.). Quelle:Textvergleich. [Web page]. Available at Cited 17 Apr 2015.
  15. Weber-Wulff, D. (n.d.). Test of plagiarism software. [Web site], prepared with assistance from Wohnsdorf, G., Pomerenke, M., Köhler, K., Möller, C., Touras, J., Zarzecki, M., & Zincke, E. Available from Cited 12 Apr 2015.
  16. Weber-Wulff, D. (2014a). False feathers—a perspective on academic plagiarism. Berlin: Springer.Google Scholar
  17. Weber-Wulff, D. (2014b). Test of the picapedia system. [Blog entry]. In Copy, shake & paste. Available from Cited 12 Apr 2015.
  18. Weber-Wulff, D., & Köhler, K. (2008). Test 2008: – S25 Eve2. [Web page]. Available at Cited 4 Apr 2015.
  19. Weber-Wulff, D., & Pomerenke, M. (2007). Eine kuriose Geschichte: Turnitin und iPlagiarismCheck 2007. Available at Cited 17 Apr 2015.
  20. Weber-Wulff, D., Köhler, K., & Möller, C. (2012). Collusion detection system test report 2012. [Web page]. Available at Cited 11 Apr 2015.
  21. Weber-Wulff, D., Möller, C., Touras, J., & Zincke, E. (2013). Plagiarism detection software test 2013. [Web page]. Available from Cited 5 Apr 2015.
  22. Wikimedia Commons. (n.d.). A database of 23,539,005 freely usable media files to which anyone can contribute. Available from Cited 17 Apr 2015.
  23. Williams, K., & Carroll, J. (2009). Referencing & understanding plagiarism. Basingstoke: Palgrave Macmillian.Google Scholar
  24. Zauner, H. (2014). Wissenschaftliches Fehlverhalten—Münsteraner Kettenplagiate. In Laborjournal, 09, 17–18.Google Scholar

Authors and Affiliations

  1. 1.FB 4, University of Applied Sciences, HTW BerlinBerlinGermany

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