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Plagiarism Detection: Methodological Approaches

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Abstract

This chapter deals with plagiarism detection. After explaining the difference between plagiarism and copyright infringement, the chapter analyses the problems that challenge the expert linguist’s work, especially the undervaluation of scientific linguistic expertise in the courts of justice, the admissibility of scientific evidence in the courts of justice and the evaluation of text similarity. Subsequently, the chapter examines plagiarism frameworks, and addresses the latest research in computer-based plagiarism detection methods and their implementation in automated plagiarism detection systems. Furthermore, the chapter points to the essential complementary role that qualitative linguistic analysis plays in plagiarism detection and draws attention to the relevance of context in understanding and interpreting the data appropriately. Lastly, the chapter provides the reader with a detailed step-by-step analysis of a live case of plagiarism between translators.

Keywords

  • Copyright infringement
  • Expert linguist
  • Forensic linguistics
  • Intelligent plagiarism
  • Literal plagiarism
  • Moral rights of the author
  • Plagiarism detection
  • Plagiarism detection systems
  • Plagiarism detection tools
  • Plagiarism between translators

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Notes

  1. 1.

    European Parliament. (2018). Copyright Law in the EU. Salient Features of Copyright Law across the EU Member States. European Parliamentary Research Service. Study. Retrieved from https://www.europarl.europa.eu/RegData/etudes/STUD/2018/625126/EPRS_STU(2018)625126_EN.pdf.

  2. 2.

    Spanish law, a civil law jurisdiction, explicitly protects the authors’ moral rights under art. 14. (Content and Characteristics of Moral Rights) of the Intellectual Property Act 1/1996: (1) The right to disclosure; (2) The right to determine how communication with the public should be effected; (3) The right to claim authorship; (4) The right to demand respect for the integrity of the work; (5) The right to modify the work with the permission of the copyright holder; (6) The right to withdraw the work due to changes in intellectual or moral convictions and (7) The right of access to the sole or rare copy of the work.

  3. 3.

    The other three enforceable limitations to the general public’s freedom of speech are patents, trademarks—and service marks—and trade secrets.

  4. 4.

    Copyright limitations are transnational in scope for most countries due to international treaties such as the Berne Convention of 1886, the UNESCO Universal Copyright Convention of 1952, the World Trade Organisation’s TRIPS Agreement of 1995 and the WIPO Copyright Treaty of 1996.

  5. 5.

    By way of example, the author has acted as an expert linguist in only four plagiarism cases over the last thirteen years, of which only two were court cases. In these two, the author acted as expert for the defendant. One case was relating to plagiarism between lawyers (Guillén-Nieto, 2020b), the other case concerned supposedly plagiarised electronic material into a teaching project.

  6. 6.

    Retrieved January, 3, 2021, from https://www.law.cornell.edu/wex/frye_standard.

  7. 7.

    Retrieved January, 3, 2021, from https://www.law.cornell.edu/wex/daubert_standard.

  8. 8.

    Retrieved from https://www.boe.es/buscar/doc.php?id=BOE-A-2000-323.

  9. 9.

    Retrieved from https://www.boe.es/buscar/act.php?id=BOE-A-1882-6036.

  10. 10.

    Kraus (2016) offers a comprehensive review of plagiarism detection systems and evaluation methods until 2012. Furthermore, Foltýnek et al. (2019) provide an exhaustive critical review evaluating the capabilities of computer-based academic plagiarism detection methods from 2013 to 2018. Over this period one can see major advances concerning the automated detection of obfuscated academic plagiarism forms.

  11. 11.

    PAN is a well-established platform for the comparative evaluation of authorship identification and plagiarism detection methods and systems. Retrieved from https://pan.webis.de/.

  12. 12.

    Retrieved from https://www.ithenticate.com/.

  13. 13.

    Retrieved from https://www.plagscan.com/es/?gclid=Cj0KCQiAx9mABhD0ARIsAEfpavR5s-cCrTnF608Lius5CnmZPtqJfK4JB0r5NZpKTjvN0OE9-mLhoBoaAoCKEALw_wcB.

  14. 14.

    Retrieved from https://www.turnitin.com/.

  15. 15.

    Retrieved from https://unicheck.com/es-es.

  16. 16.

    Retrieved from https://www.articlechecker.com/.

  17. 17.

    Retrieved from https://www.copyscape.com/.

  18. 18.

    Retrieved from https://antiplagiarist.softonic.com/.

  19. 19.

    Retrieved from https://www.duplichecker.com/.

  20. 20.

    Retrieved from https://www.plagium.com/.

  21. 21.

    Retrieved from https://plag.co/.

  22. 22.

    For confidential reasons, reference to the suspect translator is omitted.

  23. 23.

    Woolls (2012) explains that ʻin order to avoid over-matching, function words, due to their high frequencies in a language, are collected together on what is termed a “stop-list” and discounted altogether for vocabulary comparison purposesʼ (p. 525).

  24. 24.

    CREA (3.2 June 2008) is a current Spanish database that contains 160,000,000 linguistic forms from written and oral texts produced in all Spanish speaking countries from 1975 until 2004. The written texts have been selected from books, journals and magazines.

  25. 25.

    CORDE is a database of diachronic Spanish. It contains 250 million linguistic forms from a wide range of genres from the Spanish language’s origins until 1974.

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Guillén-Nieto, V. (2022). Plagiarism Detection: Methodological Approaches. In: Guillén-Nieto, V., Stein, D. (eds) Language as Evidence. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-84330-4_10

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