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DOI: 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 with the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection endeavours, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is actually a highly active research field. Over the period we review, the field has seen big improvements regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements generally originate from better semantic text analysis methods, the investigation of non-textual content features, as well as application of machine learning.

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However, in those late, coffee-fueled hours, are you presently fully assured that you correctly cited every one of the different sources you used? Do you think you're sure you didn’t accidentally forget any? Are you presently self-confident that your teacher’s plagiarism tool will give your paper a 0% plagiarism score?

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We categorize plagiarism detection methods and structure their description according to our typology of plagiarism. Lexical detection methods

We hope that our findings will help while in the development of more effective and effective plagiarism detection methods and system that will then aid the implementation of plagiarism policies.

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Prepostseo's plagiarism checker detects duplicate or copied content from your documents and shows the percentage of plagiarism along with the source.

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Support vector machine (SVM) may be the most popular model type for plagiarism detection duties. SVM employs statistical learning to minimize the distance between a hyperplane and also the training data. Selecting the hyperplane is the primary challenge for correct data classification [66].

We outlined the limitations of text-based plagiarism detection methods and prompt that future research should focus on semantic analysis approaches that also include non-textual document features, for example academic citations.

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