  Identifying academic plagiarism is a pressing problem, among others, for
research institutions, publishers, and funding organizations. Detection
approaches proposed so far analyze lexical, syntactical, and semantic text
similarity. These approaches find copied, moderately reworded, and literally
translated text. However, reliably detecting disguised plagiarism, such as
strong paraphrases, sense-for-sense translations, and the reuse of non-textual
content and ideas, is an open research problem.
  The thesis addresses this problem by proposing plagiarism detection
approaches that implement a different concept: analyzing non-textual content in
academic documents, specifically citations, images, and mathematical content.
  To validate the effectiveness of the proposed detection approaches, the
thesis presents five evaluations that use real cases of academic plagiarism and
exploratory searches for unknown cases.
  The evaluation results show that non-textual content elements contain a high
degree of semantic information, are language-independent, and largely immutable
to the alterations that authors typically perform to conceal plagiarism.
Analyzing non-textual content complements text-based detection approaches and
increases the detection effectiveness, particularly for disguised forms of
academic plagiarism.
  To demonstrate the benefit of combining non-textual and text-based detection
methods, the thesis describes the first plagiarism detection system that
integrates the analysis of citation-based, image-based, math-based, and
text-based document similarity. The system's user interface employs
visualizations that significantly reduce the effort and time users must invest
in examining content similarity.
