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Analyzing Non-Textual Content Elements to Detect Academic Plagiarism

  • Book
  • © 2023

Overview

Authors:
  1. Norman Meuschke

    1. University of Göttingen, Göttingen, Germany

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Introduction

    • Norman Meuschke
    Pages 1-7

  3. Academic Plagiarism Detection

    • Norman Meuschke
    Pages 9-77

  4. Citation-based Plagiarism Detection

    • Norman Meuschke
    Pages 79-120

  5. Image-based Plagiarism Detection

    • Norman Meuschke
    Pages 121-148

  6. Math-based Plagiarism Detection

    • Norman Meuschke
    Pages 149-178

  7. Hybrid Plagiarism Detection System

    • Norman Meuschke
    Pages 179-196

  8. Conclusion and Future Work

    • Norman Meuschke
    Pages 197-218

  9. Back Matter

    Pages 219-272
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Keywords

About this book

Identifying plagiarism is a pressing problem for research institutions, publishers, and funding bodies. Current detection methods focus on textual analysis and find copied, moderately reworded, or translated content. However, detecting more subtle forms of plagiarism, including strong paraphrasing, sense-for-sense translations, or the reuse of non-textual content and ideas, remains a challenge. This book presents a novel approach to address this problem—analyzing non-textual elements in academic documents, such as citations, images, and mathematical content. The proposed detection techniques are validated in five evaluations using confirmed plagiarism cases and exploratory searches for new instances. The results show that non-textual elements contain much semantic information, are language-independent, and resilient to typical tactics for concealing plagiarism. Incorporating non-textual content analysis complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of plagiarism. The book introduces the first integrated plagiarism detection system that combines citation, image, math, and text similarity analysis. Its user interface features visual aids that significantly reduce the time and effort users must invest in examining content similarity.

Authors and Affiliations

  • University of Göttingen, Göttingen, Germany

    Norman Meuschke

About the author

Norman Meuschke is a Senior Researcher for Information Retrieval and Natural Language Processing at the University of Göttingen, Germany.

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