@inproceedings{MeuschkeSSK19,
	address = {Urbana-Champaign, Illinois, USA},
	title = {Improving {Academic} {Plagiarism} {Detection} for {STEM} {Documents} by {Analyzing} {Mathematical} {Content} and {Citations}},
	isbn = {978-1-72811-547-4},
	url = {paper=https://www.gipp.com/wp-content/papercite-data/pdf/meuschke2019.pdf code/data=https://purl.org/hybridPD},
	doi = {10.1109/JCDL.2019.00026},
	abstract = {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 ofconcealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detec- tion process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similar- ity 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 plagia- rism. 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 text- based detection approaches for academic literature in the STEM disciplines. The data and code of our study are openly available at https://purl.org/hybridPD},
	booktitle = {Proceedings of the {Annual} {International} {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
	author = {Meuschke, Norman and Stange, Vincent and Schubotz, Moritz and Kramer, Michael and Gipp, Bela},
	month = jun,
	year = {2019},
	note = {Venue Rating: CORE A*},
	keywords = {Plagiarism Detection},
	pages = {120--129},
}

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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 ofconcealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detec- tion process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similar- ity 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 plagia- rism. 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 text- based detection approaches for academic literature in the STEM disciplines. The data and code of our study are openly available at https://purl.org/hybridPD","booktitle":"Proceedings of the Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL)","author":[{"propositions":[],"lastnames":["Meuschke"],"firstnames":["Norman"],"suffixes":[]},{"propositions":[],"lastnames":["Stange"],"firstnames":["Vincent"],"suffixes":[]},{"propositions":[],"lastnames":["Schubotz"],"firstnames":["Moritz"],"suffixes":[]},{"propositions":[],"lastnames":["Kramer"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Gipp"],"firstnames":["Bela"],"suffixes":[]}],"month":"June","year":"2019","note":"Venue Rating: CORE A*","keywords":"Plagiarism Detection","pages":"120–129","bibtex":"@inproceedings{MeuschkeSSK19,\n\taddress = {Urbana-Champaign, Illinois, USA},\n\ttitle = {Improving {Academic} {Plagiarism} {Detection} for {STEM} {Documents} by {Analyzing} {Mathematical} {Content} and {Citations}},\n\tisbn = {978-1-72811-547-4},\n\turl = {paper=https://www.gipp.com/wp-content/papercite-data/pdf/meuschke2019.pdf code/data=https://purl.org/hybridPD},\n\tdoi = {10.1109/JCDL.2019.00026},\n\tabstract = {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 ofconcealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detec- tion process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similar- ity 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 plagia- rism. 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 text- based detection approaches for academic literature in the STEM disciplines. 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