Internet of Things and Cloud Computing
Volume 7, Issue 1, March 2019, Pages: 19-24
Received: Apr. 10, 2019;
Published: May 23, 2019
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Ghadah Mohammed Abdullah Adel, Computer Science and Technology, Xidian University, Xi’an, China
Yuping Wang, Computer Science and Technology, Xidian University, Xi’an, China
Plagiarism affects education quality, academic research results and publishers reputation. Consequently, many online plagiarism tools have been developed to detect and reduce such affects. However, most of these tools were evaluated according to their abilities to reveal different rates of plagiarism in English text. While evaluating their capability in detecting different plagiarism rates from different patterns in Arabic text is still vague. This paper aims to evaluate the efficiency level of online academic plagiarism detection tools (PlagScan, iThenticate and CheckForPlagiarism.net) in detecting different plagiarism patterns’ amounts in Arabic language. A comparison was made between, PlagScan, iThenticate and CheckForPlagiarism.net, detection capabilities by merging university theses and dissertations with eight plagiarism patterns (whole document, some parts, insertion, sentence split or join, phrase reordering, syntax, lexical and morpho-syntactic) with the ratio between 90% , 30% and 10% respectively. Experiment’s results showed that iThenticate is the most efficient online plagiarism detection tool in Arabic for eight plagiarism patterns between 90% and 80% ratio Arabic language. While none of the three online plagiarism detection tools are efficient for less than 80% plagiarized text from any of the eight plagiarism patterns. Hence, mechanism enhancements and consideration to the Arabic anguage structure are recommended for online plagiarism detection tool in Arabic.
Ghadah Mohammed Abdullah Adel,
Effectiveness Level of Online Plagiarism Detection Tools in Arabic, Internet of Things and Cloud Computing.
Vol. 7, No. 1,
2019, pp. 19-24.
Ali, A. M. E. T., Abdulla, H. M. D., & Snasel, V. (2011, May). Survey of plagiarism detection methods. In 2011 Fifth Asia Modelling Symposium (pp. 39-42). IEEE.
Honig, B., & Bedi, A. (2012). The fox in the hen house: A critical examination of plagiarism among members of the Academy of Management. Academy of Management Learning & Education, 11 (1), 101-123.
Plagiarism. (n. d.). turnitin. Retrieved from http:// www.turnitin.com/.
Alzahrani, S. M., Salim, N., & Abraham, A. (2012). Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42 (2), 133-149.
IEEE (n. d). Plagiarism Levels and Corrective Actions, as taken from Section 8.2.4.D of the PSPB Operations Manual. Retrieved November 17, 2018 from https://www.ieee.org/content/dam/ieee.org/ieee/web/org/pubs/Level_description.pdf.
Abakush, I. (2016). Methods and tools for plagiarism detection in Arabic documents. In Sinteza 2016-International Scientific Conference on ICT and E-Business Related Research (pp. 173-178). Singidunum University.
Beth Calvano (2012). Plagiarism in Higher Education Research. Retrieved from http://www.ithenticate.com /plagiarism-detection-blog/bid/87315/Plagiarism-in-Higher-Education-Research.
Lukashenko, R., Graudina, V., & Grundspenkis, J. (2007, June). Computer-based plagiarism detection methods and tools: an overview. In Proceedings of the 2007 international conference on Computer systems and technologies (p. 40). ACM.
Ghadah M. Adel, Abdullatif Ghallab (2014). Performance Comparisons on Online Plagiarism Detection Software in Arabic Theses. International Conference on e-Commerce, e-Administration, e-Society, e-Education and e-Technology (e-CASE & e-Tech 2014), Nagoya University, Japan.
Naik, R. R., Landge, M. B., & Mahender, C. N. (2015). A review on plagiarism detection tools. International Journal of Computer Applications, 125 (11).
Ali, A. M. E. T., Abdulla, H. M. D., & Snasel, V. (2011). Overview and Comparison of Plagiarism Detection Tools. In DATESO (pp. 161-172).
Chow, T. W., & Rahman, M. K. M. (2009). Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection. IEEE Transactions on Neural Networks, 20 (9), 1385-1402.
Osman, A. H., Salim, N., & Abuobieda, A. (2012). Survey of text plagiarism detection. Computer Engineering and Applications Journal (ComEngApp), 1 (1), 37-45.
Ali, Y. A. A. (2018). A Model and Framework for Plagiarism Detection in Arabic Documents in Arabic Language (Doctoral dissertation, Sudan University of Science & Technology).
Y. A. Abdelrahman, et al., "A Method For Arabic Documents Plagiarism Detection," International Journal of Computer Science and Information Security, vol. 15, p. 79, 2017.
Alzahrani, Salha., Salim, Naomie (2008). Plagiarism detection in arabic scripts using fuzzy information retrieval, In Student Conference on Research and Development Student, 281, p. 1–4, 2008.
Menai, M. E. B., & Bagais, M. (2011, August). APlag: A plagiarism checker for Arabic texts. In 2011 6th International Conference on Computer Science & Education (ICCSE) (pp. 1379-1383). IEEE.
Khorsi, A., Cherroun, H., & Schwab, D. (2018). A Two-Level Plagiarism Detection System for Arabic Documents. Cybernetics and Information Technologies, 20.
Ashraf S Hussein. A plagiarism detection system for arabic documents. In Intelligent Systems’ 2014, Springer International Publishing, 2015. p. 541-552.
Ceska, Z. (2008, August). Plagiarism detection based on singular value decomposition. In International Conference on Natural Language Processing (pp. 108-119). Springer, Berlin, Heidelberg.
Bull, J., Colins, C., Coughlin, E., & Sharp, D. (2007). Technical review of plagiarism detection software report.
Weber-Wulff, D. (2014). False feathers: A perspective on academic plagiarism. Springer Science & Business.
McCabe, D. L. (2005). Cheating among college and university students: A North American perspective. International Journal for Educational Integrity, 1 (1).