Unraveling the Landscape of Bitcoin Research Using Machine Learning: A Topic Modelling Approach

This study provides a comprehensive analysis of the evolving field of Bitcoin research through the application of bibliometric analysis and structural topic modeling techniques. A dataset of 1,937 articles from the Scopus database, spanning the period between 2013 and 2023, was examined, with a specific focus on the prominent digital currency, Bitcoin. The analysis encompasses publication trends, influential journals, authors, institutions, and impactful articles in the field. Through the application of structural topic modeling, six distinct thematic clusters in Bitcoin research are identified, encompassing topics such as blockchain-based digital currency, volatility modeling, portfolio diversification, Bitcoin futures trading, return forecasting, and cryptocurrency regulations. Furthermore, this study outlines future research directions in the domains of finance, economics, and management pertaining to Bitcoin. By bridging the gap in existing literature and providing valuable insights, this study aims to support the development of effective risk management strategies, regulatory frameworks, and business approaches. The findings serve as a valuable resource for industry practitioners, academics, regulators, and policymakers navigating the evolving landscape of cryptocurrencies.