Big Data, Chatbots, and Data Mining: Transforming Academic Libraries – A Bibliometric Analysis
Md Nurul Islam, Khairul Islam, Walton Wider, Sujin Butdiswan, Shakil Ahmad, Rupak Chakrabarty, Md. Abdul Matin, Nur Ahmad · New Review of Academic Librarianship · 2025
This bibliometric analysis investigates the integration of big data, chatbots, and data mining in academic libraries, aiming to understand current trends, regional disparities, and future implications. Using comprehensive bibliometric methods, the study analyzes publications and citations from 2014 to the present, highlighting the significant growth in research and application of these technologies. The findings reveal a substantial increase in research output, with China leading in publication volume, followed by the United States and other Asian countries. The study identifies the critical role of big data in enhancing operational efficiencies, chatbots in improving user interactions, and data mining in uncovering hidden trends. However, challenges such as the digital divide and data privacy concerns are prominent. The study concludes that while these technologies offer transformative potential, addressing infrastructural disparities and implementing robust data governance frameworks are essential. The implications for researchers, practitioners, policymakers, and academic institutions include promoting interdisciplinary research, enhancing digital literacy, and fostering international collaboration. Embracing these technologies can significantly improve the role of academic libraries as dynamic, user-centric centers of knowledge and learning, effectively meeting the evolving needs of their users in the digital age.