Effectiveness Level of Online Plagiarism Detection Tools in Arabic

Ghadah Mohammed Abdullah Adel; Yuping Wang

doi:doi:10.11648/j.iotcc.20190701.13

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Effectiveness Level of Online Plagiarism Detection Tools in Arabic

Ghadah Mohammed Abdullah Adel, Yuping Wang

Published in Internet of Things and Cloud Computing (Volume 7, Issue 1)

Received: 10 April 2019 Published: 23 May 2019

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Abstract

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.

Published in	Internet of Things and Cloud Computing (Volume 7, Issue 1)
DOI	10.11648/j.iotcc.20190701.13
Page(s)	19-24
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), 2019. Published by Science Publishing Group

Keywords

Academic Plagiarism, Plagiarism Levels and Patterns, Online Plagiarism Detection Tools, Arabic Plagiarism Detection, Effectiveness of Plagiarism Detection Tools

References

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APA Style

Ghadah Mohammed Abdullah Adel, Yuping Wang. (2019). Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet of Things and Cloud Computing, 7(1), 19-24. https://doi.org/10.11648/j.iotcc.20190701.13

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ACS Style

Ghadah Mohammed Abdullah Adel; Yuping Wang. Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet Things Cloud Comput. 2019, 7(1), 19-24. doi: 10.11648/j.iotcc.20190701.13

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AMA Style

Ghadah Mohammed Abdullah Adel, Yuping Wang. Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet Things Cloud Comput. 2019;7(1):19-24. doi: 10.11648/j.iotcc.20190701.13

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@article{10.11648/j.iotcc.20190701.13,
  author = {Ghadah Mohammed Abdullah Adel and Yuping Wang},
  title = {Effectiveness Level of Online Plagiarism Detection Tools in Arabic},
  journal = {Internet of Things and Cloud Computing},
  volume = {7},
  number = {1},
  pages = {19-24},
  doi = {10.11648/j.iotcc.20190701.13},
  url = {https://doi.org/10.11648/j.iotcc.20190701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20190701.13},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Effectiveness Level of Online Plagiarism Detection Tools in Arabic
AU  - Ghadah Mohammed Abdullah Adel
AU  - Yuping Wang
Y1  - 2019/05/23
PY  - 2019
N1  - https://doi.org/10.11648/j.iotcc.20190701.13
DO  - 10.11648/j.iotcc.20190701.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 19
EP  - 24
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20190701.13
AB  - 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.
VL  - 7
IS  - 1
ER  -

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Author Information

Ghadah Mohammed Abdullah Adel

Computer Science and Technology, Xidian University, Xi’an, China
Yuping Wang

Computer Science and Technology, Xidian University, Xi’an, China

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Plain Text BibTeX RIS

APA Style

Ghadah Mohammed Abdullah Adel, Yuping Wang. (2019). Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet of Things and Cloud Computing, 7(1), 19-24. https://doi.org/10.11648/j.iotcc.20190701.13

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ACS Style

Ghadah Mohammed Abdullah Adel; Yuping Wang. Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet Things Cloud Comput. 2019, 7(1), 19-24. doi: 10.11648/j.iotcc.20190701.13

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AMA Style

Ghadah Mohammed Abdullah Adel, Yuping Wang. Effectiveness Level of Online Plagiarism Detection Tools in Arabic. Internet Things Cloud Comput. 2019;7(1):19-24. doi: 10.11648/j.iotcc.20190701.13

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@article{10.11648/j.iotcc.20190701.13,
  author = {Ghadah Mohammed Abdullah Adel and Yuping Wang},
  title = {Effectiveness Level of Online Plagiarism Detection Tools in Arabic},
  journal = {Internet of Things and Cloud Computing},
  volume = {7},
  number = {1},
  pages = {19-24},
  doi = {10.11648/j.iotcc.20190701.13},
  url = {https://doi.org/10.11648/j.iotcc.20190701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20190701.13},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Effectiveness Level of Online Plagiarism Detection Tools in Arabic
AU  - Ghadah Mohammed Abdullah Adel
AU  - Yuping Wang
Y1  - 2019/05/23
PY  - 2019
N1  - https://doi.org/10.11648/j.iotcc.20190701.13
DO  - 10.11648/j.iotcc.20190701.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 19
EP  - 24
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20190701.13
AB  - 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.
VL  - 7
IS  - 1
ER  -

Copy | Download