  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 text-based detection approaches for academic literature in the
STEM disciplines.
