[Paper Review] Plagiarism: Taxonomy, Tools and Detection Techniques

Hussain Ahmed Chowdhury, Dhruba K. Bhattacharyya|arXiv (Cornell University)|Jan 19, 2018

Academic integrity and plagiarism40 references40 citations

TL;DR

The paper surveys plagiarism forms, surveys detection tools and discusses machine learning approaches, highlighting challenges and research directions.

ABSTRACT

To detect plagiarism of any form, it is essential to have broad knowledge of its possible forms and classes, and existence of various tools and systems for its detection. Based on impact or severity of damages, plagiarism may occur in an article or in any production in a number of ways. This survey presents a taxonomy of various plagiarism forms and include discussion on each of these forms. Over the years, a good number tools and techniques have been introduced to detect plagiarism. This paper highlights few promising methods for plagiarism detection based on machine learning techniques. We analyse the pros and cons of these methods and finally we highlight a list of issues and research challenges related to this evolving research problem.

Motivation & Objective

• Provide a comprehensive taxonomy of plagiarism forms and their impact.
• Review existing tools and systems for plagiarism detection.
• Highlight machine learning techniques that show promise for detection.
• Analyze pros and cons of detection methods.
• Identify open issues and research challenges in plagiarism detection.

Proposed method

• Develop a taxonomy of plagiarism forms and discuss their characteristics and impact.
• Review and critique existing plagiarism detection tools and systems.
• Highlight and evaluate machine learning-based detection methods and their trade-offs.
• Analyze advantages and limitations of different detection approaches.
• Outline research challenges and future directions in the field.

Experimental results

Research questions

• RQ1 What are the forms and classes of plagiarism and their typical damages?
• RQ2 What tools and systems exist for plagiarism detection and how do they compare?
• RQ3 Which machine learning techniques show promise for detecting plagiarism and what are their pros and cons?
• RQ4 What are the current gaps and research challenges in plagiarism detection?

Key findings

• The paper presents a taxonomy of plagiarism forms and discusses each form.
• It reviews tools and systems available for plagiarism detection.
• It highlights machine learning techniques as promising approaches and analyzes their pros and cons.
• It identifies a set of issues and research challenges related to plagiarism detection.
• The work is anchored in proceedings from NACLIN 2016 and provides a synthesis of evolving methods.