Analyzing Non-Textual Content Elements to Detect Academic Plagiarism

TL;DR

This paper introduces a novel approach to academic plagiarism detection by analyzing non-textual content like citations, images, and mathematical expressions, which enhances detection of disguised plagiarism forms that evade traditional text-based methods.

Contribution

It proposes and validates a new multi-modal plagiarism detection system that combines non-textual content analysis with traditional text similarity measures, improving detection effectiveness.

Findings

Non-textual content contains high semantic information.

Non-textual content is language-independent and resistant to concealment.

Combining non-textual and text-based methods improves detection accuracy.

Abstract

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.