A Global Bibliometric Mapping of Plagiarism Detection Research (1990–2024)

Abstract

Plagiarism detection has gained increasing scholarly attention due to the rapid expansion of digital content and the widespread availability of online academic resources. Despite the growing body of literature, a comprehensive and up-to-date bibliometric overview that maps the intellectual structure and research evolution of plagiarism detection remains limited. To address this gap, this study conducts a global bibliometric analysis of plagiarism detection research published between 1990 and 2024. The objectives of the study were (1) to perform a descriptive analysis of the field of plagiarism detection, and (2) to identify areas of study that necessitate further investigation in order to advance plagiarism detection. Bibliometric analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to analyze the data. The study considered 305 articles from 182 journals in the Scopus database, which demonstrate the topic’s dynamic growth over the selected period. The study utilized Biblioshiny and VOSviewer to visualize data. The study identifies the most relevant sources, the distribution of publications across countries (both single-country and multi-country publications), and the most distinguished scholars whose contributions have been the most fruitful over time. Worldwide scholarly interest has focused on different types of technologies such as machine learning, deep learning, natural language processing, and text processing to detect plagiarism. Additionally, the study identified four clusters representing plagiarism detection techniques related to artificial intelligence, text processing techniques, plagiarism types, and underlying computer programming theories. Furthermore, additional research is necessary in the areas of computational linguistics, feature extraction, text processing, classification of information, semantics, syntactic analysis, and source code plagiarism detection. Overall, the insights derived from this bibliometric analysis provide a structured research agenda that supports the development of more effective, scalable, and theoretically grounded plagiarism detection methodologies.