Using structural information and citation evidence to detect significant plagiarism cases in scientific publications.
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| Title: | Using structural information and citation evidence to detect significant plagiarism cases in scientific publications. |
|---|---|
| Authors: | Alzahrani, Salha1, Palade, Vasile2, Salim, Naomie3, Abraham, Ajith4 |
| Source: | Journal of the American Society for Information Science & Technology. Feb2012, Vol. 63 Issue 2, p286-312. 27p. 3 Color Photographs, 3 Diagrams, 14 Charts, 2 Graphs. |
| Subjects: | Analysis of variance, Confidence intervals, Information retrieval, Research methodology, Plagiarism, Probability theory, Questionnaires, Science, Serial publications, Statistical hypothesis testing, T-test (Statistics), Citation analysis |
| Abstract: | In plagiarism detection (PD) systems, two important problems should be considered: the problem of retrieving candidate documents that are globally similar to a document q under investigation, and the problem of side-by-side comparison of q and its candidates to pinpoint plagiarized fragments in detail. In this article, the authors investigate the usage of structural information of scientific publications in both problems, and the consideration of citation evidence in the second problem. Three statistical measures namely Inverse Generic Class Frequency, Spread, and Depth are introduced to assign a degree of importance (i.e., weight) to structural components in scientific articles. A term-weighting scheme is adjusted to incorporate component-weight factors, which is used to improve the retrieval of potential sources of plagiarism. A plagiarism screening process is applied based on a measure of resemblance, in which component-weight factors are exploited to ignore less or nonsignificant plagiarism cases. Using the notion of citation evidence, parts with proper citation evidence are excluded, and remaining cases are suspected and used to calculate the similarity index. The authors compare their approach to two flat-based baselines, TF-IDF weighting with a Cosine coefficient, and shingling with a Jaccard coefficient. In both baselines, they use different comparison units with overlapping measures for plagiarism screening. They conducted extensive experiments using a dataset of 15,412 documents divided into 8,657 source publications and 6,755 suspicious queries, which included 18,147 plagiarism cases inserted automatically. Component-weight factors are assessed using precision, recall, and F-measure averaged over a 10-fold cross-validation and compared using the ANOVA statistical test. Results from structural-based candidate retrieval and plagiarism detection are evaluated statistically against the flat baselines using paired- t tests on 10-fold cross-validation runs, which demonstrate the efficacy achieved by the proposed framework. An empirical study on the system's response shows that structural information, unlike existing plagiarism detectors, helps to flag significant plagiarism cases, improve the similarity index, and provide human-like plagiarism screening results. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 76402668 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Using structural information and citation evidence to detect significant plagiarism cases in scientific publications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alzahrani%2C+Salha%22">Alzahrani, Salha</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Palade%2C+Vasile%22">Palade, Vasile</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Salim%2C+Naomie%22">Salim, Naomie</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Abraham%2C+Ajith%22">Abraham, Ajith</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Feb2012, Vol. 63 Issue 2, p286-312. 27p. 3 Color Photographs, 3 Diagrams, 14 Charts, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Plagiarism%22">Plagiarism</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Science%22">Science</searchLink><br /><searchLink fieldCode="DE" term="%22Serial+publications%22">Serial publications</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+analysis%22">Citation analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In plagiarism detection (PD) systems, two important problems should be considered: the problem of retrieving candidate documents that are globally similar to a document q under investigation, and the problem of side-by-side comparison of q and its candidates to pinpoint plagiarized fragments in detail. In this article, the authors investigate the usage of structural information of scientific publications in both problems, and the consideration of citation evidence in the second problem. Three statistical measures namely Inverse Generic Class Frequency, Spread, and Depth are introduced to assign a degree of importance (i.e., weight) to structural components in scientific articles. A term-weighting scheme is adjusted to incorporate component-weight factors, which is used to improve the retrieval of potential sources of plagiarism. A plagiarism screening process is applied based on a measure of resemblance, in which component-weight factors are exploited to ignore less or nonsignificant plagiarism cases. Using the notion of citation evidence, parts with proper citation evidence are excluded, and remaining cases are suspected and used to calculate the similarity index. The authors compare their approach to two flat-based baselines, TF-IDF weighting with a Cosine coefficient, and shingling with a Jaccard coefficient. In both baselines, they use different comparison units with overlapping measures for plagiarism screening. They conducted extensive experiments using a dataset of 15,412 documents divided into 8,657 source publications and 6,755 suspicious queries, which included 18,147 plagiarism cases inserted automatically. Component-weight factors are assessed using precision, recall, and F-measure averaged over a 10-fold cross-validation and compared using the ANOVA statistical test. Results from structural-based candidate retrieval and plagiarism detection are evaluated statistically against the flat baselines using paired- t tests on 10-fold cross-validation runs, which demonstrate the efficacy achieved by the proposed framework. An empirical study on the system's response shows that structural information, unlike existing plagiarism detectors, helps to flag significant plagiarism cases, improve the similarity index, and provide human-like plagiarism screening results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asi.21651 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 286 Subjects: – SubjectFull: Analysis of variance Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Plagiarism Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Questionnaires Type: general – SubjectFull: Science Type: general – SubjectFull: Serial publications Type: general – SubjectFull: Statistical hypothesis testing Type: general – SubjectFull: T-test (Statistics) Type: general – SubjectFull: Citation analysis Type: general Titles: – TitleFull: Using structural information and citation evidence to detect significant plagiarism cases in scientific publications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alzahrani, Salha – PersonEntity: Name: NameFull: Palade, Vasile – PersonEntity: Name: NameFull: Salim, Naomie – PersonEntity: Name: NameFull: Abraham, Ajith IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 15322882 Numbering: – Type: volume Value: 63 – Type: issue Value: 2 Titles: – TitleFull: Journal of the American Society for Information Science & Technology Type: main |
| ResultId | 1 |
