survey

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Academic Plagiarism Detection: A Systematic Literature Review

Authors:

Tomáš Foltýnek,

Norman Meuschke,

Bela GippAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 6

Article No.: 112, Pages 1 - 42

https://doi.org/10.1145/3345317

Published: 16 October 2019 Publication History

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Abstract

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.

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 6

November 2020

806 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3368196

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Publication History

Published: 16 October 2019

Accepted: 01 August 2019

Revised: 01 August 2019

Received: 01 March 2019

Published in CSUR Volume 52, Issue 6

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