Investigating various cryptocurrency research trends: an analysis employing text mining and topic modeling

Abstract

Purpose

The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain.

Design/methodology/approach

Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA).

Findings

The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section.

Originality/value

While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.

Citation

Singh, A., Trivedi, S.K., Vishnu, S., T., H. and Zhang, J.Z. (2024), "Investigating various cryptocurrency research trends: an analysis employing text mining and topic modeling", Global Knowledge, Memory and Communication, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/GKMC-02-2024-0073