Testing of Support Tools for Plagiarism Detection
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Abstract (arXiv)
There is a general belief that software must be able to easily do things that humans find difficult. Since finding sources for plagiarism in a text is not an easy task, there is a wide-spread expectation that it must be simple for software to determine if a text is plagiarized or not. Software cannot determine plagiarism, but it can work as a support tool for identifying some text similarity that may constitute plagiarism. But how well do the various systems work? This paper reports on a collaborative test of 15 web-based text-matching systems that can be used when plagiarism is suspected. It was conducted by researchers from seven countries using test material in eight different languages, evaluating the effectiveness of the systems on single-source and multi-source documents. A usability examination was also performed. The sobering results show that although some systems can indeed help identify some plagiarized content, they clearly do not find all plagiarism and at times also identify non-plagiarized material as problematic.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2020.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 1.
11 papers.
- 2023Testing of Detection Tools for AI-Generated Textcited by 86
- 2022How Large Language Models are Transforming Machine-Paraphrased Plagiarismcited by 38
- 2021Identifying Machine-Paraphrased Plagiarismcited by 16
- 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
- 2021Corpus-Based Paraphrase Detection Experiments and Reviewcited by 5
- 2023Assisting Human Decisions in Document Matchingcited by 3
- 2021STEREO: Scientific Text Reuse in Open Access Publicationscited by 1
- 2022A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languagescited by 1
- 2021Taxonomy of academic plagiarism methodscited by 0
- 2023Word Definitions from Large Language Modelscited by 0
- 2024Responsible AI for Test Equity and Quality: The Duolingo English Test as a Case Studycited by 0
- 2024Responsible AI for Test Equity and Quality: The Duolingo English Test as a Case Study28 Aug 2024
- 2023Word Definitions from Large Language Models10 Nov 2023
- 2023Testing of Detection Tools for AI-Generated Text21 Jun 2023
- 2023Assisting Human Decisions in Document Matching16 Feb 2023
- 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methods13 Dec 2022
- 2022How Large Language Models are Transforming Machine-Paraphrased Plagiarism7 Oct 2022
- 2022A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages10 Jan 2022
- 2021STEREO: Scientific Text Reuse in Open Access Publications22 Dec 2021
- 2021Corpus-Based Paraphrase Detection Experiments and Review31 May 2021
- 2021Taxonomy of academic plagiarism methods25 May 2021
- 2021Identifying Machine-Paraphrased Plagiarism22 Mar 2021