Academic Plagiarism Detection : A Systematic Literature Review

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FOLTÝNEK, Tomáš, Norman MEUSCHKE, Bela GIPP, 2020. Academic Plagiarism Detection : A Systematic Literature Review. In: ACM Computing Surveys. Association for Computing Machinery (ACM). 52(6), 112. ISSN 0360-0300. eISSN 1557-7341. Available under: doi: 10.1145/3345317

@article{Foltynek2020-01-21Acade-49953, title={Academic Plagiarism Detection : A Systematic Literature Review}, year={2020}, doi={10.1145/3345317}, number={6}, volume={52}, issn={0360-0300}, journal={ACM Computing Surveys}, author={Foltýnek, Tomáš and Meuschke, Norman and Gipp, Bela}, note={Article Number: 112} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:issued>2020-01-21</dcterms:issued> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Gipp, Bela</dc:contributor> <dc:contributor>Foltýnek, Tomáš</dc:contributor> <dc:rights>terms-of-use</dc:rights> <dcterms:available rdf:datatype="">2020-06-22T08:10:42Z</dcterms:available> <dcterms:hasPart rdf:resource=""/> <dc:contributor>Meuschke, Norman</dc:contributor> <dc:creator>Foltýnek, Tomáš</dc:creator> <dcterms:title>Academic Plagiarism Detection : A Systematic Literature Review</dcterms:title> <dspace:isPartOfCollection rdf:resource=""/> <dspace:hasBitstream rdf:resource=""/> <bibo:uri rdf:resource=""/> <dc:creator>Meuschke, Norman</dc:creator> <dcterms:abstract xml:lang="eng">This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.</dcterms:abstract> <dc:creator>Gipp, Bela</dc:creator> <dcterms:rights rdf:resource=""/> <dc:date rdf:datatype="">2020-06-22T08:10:42Z</dc:date> <dc:language>eng</dc:language> <dcterms:isPartOf rdf:resource=""/> </rdf:Description> </rdf:RDF>

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