Plagiarized research papers can skew meta-studies and thus jeopardize patient safety. For example, in medicine or pharmacology, meta-studies are an important tool to assess the efficacy and safety of medical drugs and treatments. Wrong findings can spread and affect later research or practical applications. If researchers expand or revise earlier findings in subsequent research, then papers that plagiarized the original paper remain unaffected. Plagiarized research papers impede the scientific process, e.g., by distorting the mechanisms for tracing and correcting results. 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.Īcademic plagiarism is one of the severest forms of research misconduct (a “cardinal sin”) and has strong negative impacts on academia and the public. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. 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. We show that academic plagiarism detection is a highly active research field. 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. This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 20.