Academic Plagiarism Detection : A Systematic Literature Review

Lade...
Vorschaubild
Dateien
Foltynek_2-c4an0qynjftp7.pdf
Foltynek_2-c4an0qynjftp7.pdfGröße: 868.24 KBDownloads: 2702
Datum
2020
Autor:innen
Foltýnek, Tomáš
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
ACM Computing Surveys. Association for Computing Machinery (ACM). 2020, 52(6), 112. ISSN 0360-0300. eISSN 1557-7341. Available under: doi: 10.1145/3345317
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690FOLTÝNEK, Tomáš, Norman MEUSCHKE, Bela GIPP, 2020. Academic Plagiarism Detection : A Systematic Literature Review. In: ACM Computing Surveys. Association for Computing Machinery (ACM). 2020, 52(6), 112. ISSN 0360-0300. eISSN 1557-7341. Available under: doi: 10.1145/3345317
BibTex
@article{Foltynek2020-01-21Acade-49953,
  year={2020},
  doi={10.1145/3345317},
  title={Academic Plagiarism Detection : A Systematic Literature Review},
  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:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/49953">
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/49953/1/Foltynek_2-c4an0qynjftp7.pdf"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <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:rights>Attribution-ShareAlike 4.0 International</dc:rights>
    <dc:creator>Gipp, Bela</dc:creator>
    <dcterms:issued>2020-01-21</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-06-22T08:10:42Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/49953/1/Foltynek_2-c4an0qynjftp7.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/49953"/>
    <dc:contributor>Foltýnek, Tomáš</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-06-22T08:10:42Z</dc:date>
    <dc:creator>Meuschke, Norman</dc:creator>
    <dc:creator>Foltýnek, Tomáš</dc:creator>
    <dc:contributor>Gipp, Bela</dc:contributor>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-sa/4.0/"/>
    <dc:contributor>Meuschke, Norman</dc:contributor>
    <dcterms:title>Academic Plagiarism Detection : A Systematic Literature Review</dcterms:title>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Unbekannt
Diese Publikation teilen