How the Cryptocurrency Discourse is Changing: A Textual Analysis

Author/s Gianfranco Tusset
Publishing Year 2024 Issue 2023/2 Language English
Pages 22 P. 31-52 File size 201 KB
DOI 10.3280/SPE2023-002002
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The paper aims to retrace the academic discourse on cryptocurrencies from 2015 to 2022 by treating it as a lexical unicum that evolves over time. The purpose is to understand what themes have emerged and how they have changed the discourse on cryptocurrencies. We used a three-step methodology. The first consists of text mining that allows us to create, from 1057 academic articles on the subject, the matrix containing the frequencies of words/n-grams. In a second step, lexical analysis is enriched by correspondence analysis, a useful tool to measure the "distance" and evolution of academic discourse and to identify significant content discontinuity. Finally, the causal analysis addresses the ultimate goal of understanding whether it is possible to define future developments in the cryptocurrency discourse, whether it will absorb instances from outside or remain focused on the prevailing themes to date. The identification and application of a method to analyze the evolution of the cryptocurrency discourse allowed us to distinguish at least two distinct phases characterized by specific content and cryptocurrencies.

Keywords: evolution of cryptocurrency lexicon; text as data; academic debate.

Jel codes: G32, B41, B52

  1. Asemi A., Ko A. (2019). A Bibliometrics Literature Review on Cryptocurrency, Research Gate. DOI: 10.13140/RG.2.2.29027.91682
  2. Atkinson J., Escudero A. (2022). Evolutionary natural-language coreference resolution for sentiment analysis, Intern. Journ, Inform. Mang. Data Ins., 2, 100115.
  3. Ba C.T., Zignani M., Gaito S. (2022). The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit, PLoS ONE, 17(6), e0267612.
  4. Bariviera A.F., Merediz-Solà I. (2021). Where do we stand in cryptocurrencies economic Research? A survey based on hybrid analysis, J. Econ. Survey, 35(2): 377-407.
  5. Beh E.J., Lombardo R. (2014). Correspondence Analysis. Theory, Practice and New Strategies. Wiley, Chichester.
  6. Bhatt A., Joshipura M., Joshipura N. (2022). Decoding the trinity of Fintech, digitalization and financial services: An integrated bibliometric analysis and thematic literature review approach, Cog. Econ. Finance, 10, 2114160.
  7. Bouteska A., Mefteh-Wali S., Dang T. (2022). Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic, Techn. Forec. Soc. Change, 184, 121999.
  8. Chen M.A., Wu D., Yang B. (2019). How Valuable Is FinTech Innovation?. Rev. Financ. Stud., 32(5).
  9. Coulter K.A. (2022). The impact of news media on Bitcoin prices: modelling data driven discourses in the crypto-economy with natural language processing, Royal Soc. Open Sci., 9, 220276.
  10. Dadar P. (2018). Decyphering cryptocurrencies: Sentiments and prices. SCSUG Paper.
  11. Egami N., Fong C.J., Grimmer J., Roberts M.E., Stewart B.M. (2018). How to Make Causal Inferences Using Texts, arXiv, 1802.02163v1.
  12. Elsayed A.H., Gozgor G., Yarovaya L. (2022). Volatility and return connectedness of cryptocurrency, gold, and uncertainty: Evidence from the cryptocurrency uncertainty indices, Financ Res. Lett., 47, 102732.
  13. Garcia‑Corral F.J., Cordero‑Garcia J.A., de Pablo‑Valenciano J., Uribe‑Toril J. (2022). A bibliometric review of cryptocurrencies: how have they grown?, Financ. Innov., 8(2).
  14. García-Medina A., Hernández J.B. (2020). Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence, Entropy, 22(7), 760.
  15. Garriga M., Dalla Palma S., Arias M., De Renzis A., Pareschi R., Tamburri D.A (2020). Blockchain and cryptocurrencies: A classification and comparison of architecture drivers, Concurrency and Computation, 33(8).
  16. Granger C.W.J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, 37, 424-438.
  17. Greenacre M. (2007). Correspondence Analysis in Practice, Chapman & Hall, Boca Raton.
  18. Grimmer J., Stewart B. (2013). Text ad Data: The promise and Pitfalls of Automatic Content Analysis Methods for Political Texts, Political Analysis, 21: 267-97.
  19. Guerrero Cusumano J.L. (2017). A Detection Mechanism with Text Mining Cross Correlation Approach, IEEE International Conference on Big Data Boston.
  20. Guo X., Donev P. (2020). Bibliometrics and Network Analysis of Cryptocurrency Research, J Syst Sci Complex, 33: 1933-1958.
  21. Gupta A., Dengre V., Kheruwala H:A., Shah M. (2020). Comprehensive review of text‑mining applications in finance, Financial Innovation, 6: 39.
  22. Hamilton J.D. (1994). Time Series Analysis, Princeton University Press, Princeton. Hassani H., Huang X., & Ghodsi M. (2018). Big Data and Causality. Annals Data Science, 5: 133-156.
  23. Hill T., Lewicki P. (2006). Statistics. Methods and Applications, StatSoft, Tulsa. Hoover K.D. (2001). Causality in Macroeconomics, Cambridge University Press, Cambridge.
  24. Jaquart P., Kopke S., Weinhardt C. (2022). Machine learning for cryptocurrency market prediction and trading, J. Financ. Data Sci., 8: 331-352.
  25. Kim Y.B., Lee J., Park N., Choo J., Kim J-H., Kim (2017). When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation. PLoS ONE, 12(5), e0177630.
  26. Kochergin D., Ivanova A. (2022). Stablecoins: Classification, functional features and development prospects, J. New Econ. Assoc., 53(1): 100-120. DOI: 10.31737/2221-2264-2022-53-1-5
  27. Köse O., Karagoz P., Gokce P. (2020). Towards a Crypto Asset Taxonomy: A Text Classification-based Approach. Research Gate, DOI: 10.1145/3415958.3433078
  28. Kraaijeveld O., De Smedt J. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices, J. Int. Financ. Mark. Inst. Money, 65, 101188 v.
  29. Kufenko V., Geiger N. (2016). Business cycles in the economy and in economics: an econometric analysis, Scientometrics, 107: 43-69.
  30. Kwapień J., Wątorek M., Drożdż S. (2021). Cryptocurrency Market Consolidation in 2020-2021, Entropy, 23(12), 1674.
  31. Abdi H., Williams L.J. (2010). Principal Component Analysis, Wiley Int. Rev. Comput. Stat., 2: 433-459.
  32. Ahn Y., Kim D. (2021). Emotional trading in the cryptocurrency market, Financ. Res. Lett., 42, 101912.
  33. Akyildirim E., Aysan A.F., Cepni O., Darendeli S.P.C. (2021). Do investor sentiments drive cryptocurrency prices?, Econ. Lett. 206, 109980.
  34. Alnafrah I., Bogdanova E., Maximova T. (2019). Text mining as a facilitating tool for deploying blockchain technology in the intellectual property rights system, Inter. J. Intell. Property Management 2: 120-135. DOI: 10.1504/IJIPM.2019.100207
  35. Laskowski M., Kim H.M. (2016). Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence, arXiv, 1611.00315v1.

Gianfranco Tusset, How the Cryptocurrency Discourse is Changing: A Textual Analysis in "HISTORY OF ECONOMIC THOUGHT AND POLICY" 2/2023, pp 31-52, DOI: 10.3280/SPE2023-002002