Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional textbased detection approaches for academic literature in the STEM disciplines. The data and code of our study are openly available at https://purl.org/hybridPD

We report on an exploratory analysis of the forms of plagiarism observable in mathematical publications, which we identified by investigating editorial notes from zbMATH. While most cases we encountered were simple copies of earlier work, we also identified several forms of disguised plagiarism. We investigated 11 cases in detail and evaluate how current plagiarism detection systems perform in identifying these cases. Moreover, we describe the steps required to discover these and potentially undiscovered cases in the future.

Current plagiarism detection systems reliably find instances of copied and moderately altered text, but often fail to detect strong paraphrases, translations, and the reuse of non-textual content and ideas. To improve upon the detection capabilities for such concealed content reuse in academic publications, we make four contributions: i) We present the first plagiarism detection approach that combines the analysis of mathematical expressions, images, citations and text. ii) We describe the implementation of this hybrid detection approach in the research prototype HyPlag. iii) We present novel visualization and interaction concepts to aid users in reviewing content similarities identified by the hybrid detection approach. iv) We demonstrate the usefulness of the hybrid detection and result visualization approaches by using HyPlag to analyze a confirmed case of content reuse present in a retracted research publication.

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We present a four-phase parallel approach for capturing, annotating, and visualizing parallel structures in XML documents. We designed a highlighting strategy that first decomposes XML documents in various data streams, including plain text, formulae, and images. Second, those streams are processed with external algorithms and tools optimized for specific tasks, such as analyzing similarities or differences or differences in the respective formats. Third, we compute comparison metadata such as annotations and highlighting marks. Fourth, the position information is concatenated based on the original XML's computed positions document. Eventually, the resulting comparison can then be visualized or processed further while keeping the reference to the source documents intact. While our algorithm has been developed for visualizing similarities as part of plagiarism detection tasks, we expect that many applications will benefit from a well-designed and integrative method that separates between addressing the match locations and inserting highlight marks. For example, our algorithm can also add comments in XML-unaware plaintext editors. We also treat the edge cases, overlaps as well as multimatch with our approach.