Detecting machine-obfuscated plagiarism

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F20%3A43917832" target="_blank" >RIV/62156489:43110/20:43917832 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-43687-2_68" target="_blank" >https://doi.org/10.1007/978-3-030-43687-2_68</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-43687-2_68" target="_blank" >10.1007/978-3-030-43687-2_68</a>

Result language
angličtina
Original language name
Detecting machine-obfuscated plagiarism
Original language description
Research on academic integrity has identified online paraphrasing tools as a severe threat to the effectiveness of plagiarism detection systems. To enable the automated identification of machine-paraphrased text, we make three contributions. First, we evaluate the effectiveness of six prominent word embedding models in combination with five classifiers for distinguishing human-written from machine-paraphrased text. The best performing classification approach achieves an accuracy of 99.0% for documents and 83.4% for paragraphs. Second, we show that the best approach outperforms human experts and established plagiarism detection systems for these classification tasks. Third, we provide a Web application that uses the best performing classification approach to indicate whether a text underwent machine-paraphrasing. The data and code of our study are openly available.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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