How are texts analyzed in blockchain research? A systematic literature review

Zhuo, Xian; Irresberger, Felix; Bostandzic, Denefa

Abstract

This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.

Citation

Zhuo, X., Irresberger, F., & Bostandzic, D. (2024). How are texts analyzed in blockchain research? A systematic literature review. Financial Innovation, 10(1), Article 60. https://doi.org/10.1186/s40854-023-00501-6

• Journal Article Type: Article
• Acceptance Date: Apr 25, 2023
• Online Publication Date: Feb 29, 2024
• Publication Date: Feb 29, 2024
• Deposit Date: May 21, 2024
• Publicly Available Date: May 21, 2024
• Journal: Financial Innovation
• Publisher: SpringerOpen
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Article Number: 60
• DOI: https://doi.org/10.1186/s40854-023-00501-6
• Keywords: Machine learning algorithm, Systematic literature review, C10, Topic modeling, O30, Blockchain, C80, Text analysis, Sentiment analysis
• Public URL: https://durham-repository.worktribe.com/output/2292417