Comparative analysis of text-based plagiarism detection techniques

• Published in: PloS one Vol. 20; no. 4; p. e0319551
• Main Authors: Sajid, Muhammad, Sanaullah, Muhammad, Fuzail, Muhammad, Malik, Tauqeer Safdar, Shuhidan, Shuhaida Mohamed
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.04.2025
• Subjects: Artificial Intelligence; Availability; Biology and Life Sciences; Changing environments; Comparative analysis; Computer and Information Sciences; Computer applications; Computers; Deepfake; Detectors; Environmental changes; Humans; Language; Large language models; Literature reviews; Machine learning; Methods; Neural networks; Physical Sciences; Plagiarism; Plagiarism detection software; Research and Analysis Methods; Sensors; Social Sciences; Software; United Kingdom; Pakistan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0319551

In text analysis, identifying plagiarism is a crucial area of study that looks for copied information in a document and determines whether or not the same author writes portions of the text. With the emergence of publicly available tools for content generation based on large language models, the problem of inherent plagiarism has grown in importance across various industries. Students are increasingly committing plagiarism as a result of the availability and use of computers in the classroom and the generally extensive accessibility of electronic information found on the internet. As a result, there is a rising need for reliable and precise detection techniques to deal with this changing environment. This paper compares several plagiarism detection techniques and looks into how well different detection systems can distinguish between content created by humans and content created by Artificial Intelligence (AI). This article systematically evaluates 189 research papers published between 2019 and 2024 to provide an overview of the research on computational approaches for plagiarism detection (PD). We suggest a new technically focused structure for efforts to prevent and identify plagiarism, types of plagiarism, and computational techniques for detecting plagiarism to organize the way the research contributions are presented. We demonstrated that the field of plagiarism detection is rife with ongoing research. Significant progress has been made in the field throughout the time we reviewed in terms of automatically identifying plagiarism that is highly obscured and hence difficult to recognize. The exploration of nontextual contents, the use of machine learning, and improved semantic text analysis techniques are the key sources of these advancements. Based on our analysis, we concluded that the combination of several analytical methodologies for textual and nontextual content features is the most promising subject for future research contributions to further improve the detection of plagiarism.