American Journal of Data Mining and Knowledge Discovery

| Peer-Reviewed |

Survey and Comparison between Plagiarism Detection Tools

Received: 09 January 2017    Accepted: 21 January 2017    Published: 21 February 2017
Views:       Downloads:

Share This Article

Abstract

With the spread of using Internet in teaching and learning, and in literature reviews, plagiarism has spread widely, where researchers take readymade researches and claim them to themselves. The action of "copy and paste" has become extensive in all fields, and the scientific research domain is no less than other spheres. This has prompted many international organizations to produce detection tools to spot plagiarism in scientific articles and research reports. Many computer programs exist to identify plagiarism in scientific papers and essays in order to establish scientific honesty and to upgrade the level of university researches. Some of these programs are free, while some are commercially available. This paper presents comparison between the most famous plagiarism detection programs. The comparison helps individuals as well as organizations to select the plagiarism detection program that is most relevant for their objectives.

DOI 10.11648/j.ajdmkd.20170202.12
Published in American Journal of Data Mining and Knowledge Discovery (Volume 2, Issue 2, June 2017)
Page(s) 50-53
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Plagiarism, Research Misconduct, Research Ethics, Internet Cheating, Plagiarism Detection Programs, Plagiarism Detection Tools

References
[1] Ali, A. M. E. T., Abdulla, H. M. D. & Snasel, V. (2011), Overview and Comparison of Plagiarism Detection Tools. In V. Snasel, J. Pokorny & K. Richta (Eds.), CEUR Workshop Proceedings (pp. 161–172), Písek, Czech Republic: VŠB-Technical University of Ostrava.
[2] Google (2016). Retrieved from http://www.google.com.
[3] Klug, B. (2014). Retrieved from http://www.dustball.com/cs/plagiarism.checker.
[4] Dupli Checker (2012). Retrieved from http://www.duplichecker.com.
[5] Plagiarisma (2015). Retrieved from http://plagiarisma.net.
[6] Academic Plagiarism (2014). Retrieved from https://academicplagiarism.com.
[7] Plagiarism Checker (2016). Retrieved from http://www.plagiarismchecker.com.
[8] PlagiServe (2015). Retrieved from http://www.plagiserve.com.
[9] EVE Plagiarism Detection System (2000). Retrieved from http://www.canexus.com/eve/index.shtml.
[10] Plag Aware (2012). Retrieved from http://www.plagaware.com.
[11] PlagScan (2016). Retrieved from http://www.plagscan.com.
[12] Academic Paradigms (2016). Retrieved from http://www.checkforplagiarism.net.
[13] Academic Plagiarism (2016). Retrieved from http://plagiarismdetection.org.
[14] Turnitin (2015a). Retrieved from http://en.writecheck.com.
[15] Turnitin (2015b). Retrieved from http://www.turnitin.com.
[16] Bull, J., Collins, C., Coughlin, E. and Sharp, D. (2001). Technical Review of Plagiarism Detection Software Report, Luton, UK: University of Luton and Computer-assisted Assessment Centre.
[17] Turnitin (2015c). Retrieved from http://www.ithenticate.com.
[18] Top Ten Reviews (2011). Retrieved from http://plagiarism-checker-review.toptenreviews.com.
Author Information
  • Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

Cite This Article
  • APA Style

    Mahmoud Nadim Nahas. (2017). Survey and Comparison between Plagiarism Detection Tools. American Journal of Data Mining and Knowledge Discovery, 2(2), 50-53. https://doi.org/10.11648/j.ajdmkd.20170202.12

    Copy | Download

    ACS Style

    Mahmoud Nadim Nahas. Survey and Comparison between Plagiarism Detection Tools. Am. J. Data Min. Knowl. Discov. 2017, 2(2), 50-53. doi: 10.11648/j.ajdmkd.20170202.12

    Copy | Download

    AMA Style

    Mahmoud Nadim Nahas. Survey and Comparison between Plagiarism Detection Tools. Am J Data Min Knowl Discov. 2017;2(2):50-53. doi: 10.11648/j.ajdmkd.20170202.12

    Copy | Download

  • @article{10.11648/j.ajdmkd.20170202.12,
      author = {Mahmoud Nadim Nahas},
      title = {Survey and Comparison between Plagiarism Detection Tools},
      journal = {American Journal of Data Mining and Knowledge Discovery},
      volume = {2},
      number = {2},
      pages = {50-53},
      doi = {10.11648/j.ajdmkd.20170202.12},
      url = {https://doi.org/10.11648/j.ajdmkd.20170202.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajdmkd.20170202.12},
      abstract = {With the spread of using Internet in teaching and learning, and in literature reviews, plagiarism has spread widely, where researchers take readymade researches and claim them to themselves. The action of "copy and paste" has become extensive in all fields, and the scientific research domain is no less than other spheres. This has prompted many international organizations to produce detection tools to spot plagiarism in scientific articles and research reports. Many computer programs exist to identify plagiarism in scientific papers and essays in order to establish scientific honesty and to upgrade the level of university researches. Some of these programs are free, while some are commercially available. This paper presents comparison between the most famous plagiarism detection programs. The comparison helps individuals as well as organizations to select the plagiarism detection program that is most relevant for their objectives.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Survey and Comparison between Plagiarism Detection Tools
    AU  - Mahmoud Nadim Nahas
    Y1  - 2017/02/21
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajdmkd.20170202.12
    DO  - 10.11648/j.ajdmkd.20170202.12
    T2  - American Journal of Data Mining and Knowledge Discovery
    JF  - American Journal of Data Mining and Knowledge Discovery
    JO  - American Journal of Data Mining and Knowledge Discovery
    SP  - 50
    EP  - 53
    PB  - Science Publishing Group
    SN  - 2578-7837
    UR  - https://doi.org/10.11648/j.ajdmkd.20170202.12
    AB  - With the spread of using Internet in teaching and learning, and in literature reviews, plagiarism has spread widely, where researchers take readymade researches and claim them to themselves. The action of "copy and paste" has become extensive in all fields, and the scientific research domain is no less than other spheres. This has prompted many international organizations to produce detection tools to spot plagiarism in scientific articles and research reports. Many computer programs exist to identify plagiarism in scientific papers and essays in order to establish scientific honesty and to upgrade the level of university researches. Some of these programs are free, while some are commercially available. This paper presents comparison between the most famous plagiarism detection programs. The comparison helps individuals as well as organizations to select the plagiarism detection program that is most relevant for their objectives.
    VL  - 2
    IS  - 2
    ER  - 

    Copy | Download

  • Sections