Identifying Machine-Paraphrased Plagiarism

doi:10.1007/978-3-030-96957-8_34

Paper

Identifying Machine-Paraphrased Plagiarism

Published Mar 22, 2021 · Jan Philip Wahle, Terry Ruas, Tom'avs Folt'ynek

ArXiv

31

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2

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Abstract

Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99% (F1 = 99.68% for SpinBot and F1 = 71.64% for SpinnerChief cases), while human evaluators achieved F1 = 78.4% for SpinBot and F1 = 65.6% for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.

Preprint

Study Snapshot

Longformer, a neural language model, effectively detects machine-paraphrased plagiarism, improving detection rates compared to human evaluators.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Identifying Machine-Paraphrased Plagiarism

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