Titolo completo
HISTORY OF ECONOMIC THOUGHT AND POLICY
Editore
FrancoAngeli
ISSN
2240-9971 (Rivista Stampata)
2280-188X (Rivista Online)
Numero del fascicolo
2
Designazione del fascicolo
2
Data del fascicolo
2023
Titolo completo
How the Cryptocurrency Discourse is Changing: A Textual Analysis
Di (autore)
Prima Pagina
31
Ultima Pagina
52
Lingua del testo
Inglese
Data di publicazione
2024/01
Copyright
2023 FrancoAngeli srl
Descrizione principale
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.
Citazione non strutturata
Abdi H., Williams L.J. (2010). Principal Component Analysis, Wiley Int. Rev. Comput. Stat., 2: 433-459.
https://doi.org/10.1002/wics.101
Citazione non strutturata
Ahn Y., Kim D. (2021). Emotional trading in the cryptocurrency market, Financ. Res. Lett., 42, 101912.
https://doi.org/10.1016/j.frl.2020.101912
Citazione non strutturata
Akyildirim E., Aysan A.F., Cepni O., Darendeli S.P.C. (2021). Do investor sentiments drive cryptocurrency prices?, Econ. Lett.
206, 109980.
https://doi.org/10.1016/j.econlet.2021.109980
Citazione non strutturata
Atkinson J., Escudero A. (2022). Evolutionary natural-language coreference resolution for sentiment analysis, Intern. Journ,
Inform. Mang. Data Ins., 2, 100115.
https://doi.org/10.1016/j.jjimei.2022.100115
Citazione non strutturata
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.
https://doi.org/10.1371/journal.pone.0267612
Citazione non strutturata
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.
https://doi.org/10.1111/joes.12412
Citazione non strutturata
Beh E.J., Lombardo R. (2014). Correspondence Analysis. Theory, Practice and New Strategies. Wiley, Chichester.
https://doi.org/10.1002/9781118762875
Citazione non strutturata
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.
https://doi.org/10.1080/23322039.2022.2114160
Citazione non strutturata
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.
https://doi.org/10.1016/j.techfore.2022.121999
Citazione non strutturata
Chen M.A., Wu D., Yang B. (2019). How Valuable Is FinTech Innovation?. Rev. Financ. Stud., 32(5).
https://doi.org/10.1093/rfs/hhy130
Citazione non strutturata
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.
https://doi.org/10.1098/rsos.220276
Citazione non strutturata
Dadar P. (2018). Decyphering cryptocurrencies: Sentiments and prices. SCSUG Paper.
Citazione non strutturata
Egami N., Fong C.J., Grimmer J., Roberts M.E., Stewart B.M. (2018). How to Make Causal Inferences Using Texts, arXiv, 1802.02163v1.
Citazione non strutturata
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.
https://doi.org/10.1016/j.frl.2022.102732
Citazione non strutturata
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).
https://doi.org/10.1186/s40854-021-00306-5
Citazione non strutturata
García-Medina A., Hernández J.B. (2020). Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of
Turbulence, Entropy, 22(7), 760.
https://doi.org/10.3390/e22070760
Citazione non strutturata
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).
https://doi.org/10.1002/cpe.5992
Citazione non strutturata
Granger C.W.J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, 37,
424-438.
https://doi.org/10.2307/1912791
Citazione non strutturata
Greenacre M. (2007). Correspondence Analysis in Practice, Chapman & Hall, Boca Raton.
https://doi.org/10.1201/9781420011234
Citazione non strutturata
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.
https://doi.org/10.1093/pan/mps028
Citazione non strutturata
Guerrero Cusumano J.L. (2017). A Detection Mechanism with Text Mining Cross Correlation Approach, IEEE International Conference on Big Data Boston.
Citazione non strutturata
Guo X., Donev P. (2020). Bibliometrics and Network Analysis of Cryptocurrency Research, J Syst Sci Complex, 33: 1933-1958.
https://doi.org/10.1007/s11424-020-9094-z
Citazione non strutturata
Gupta A., Dengre V., Kheruwala H:A., Shah M. (2020). Comprehensive review of text‑mining applications in finance, Financial
Innovation, 6: 39.
https://doi.org/10.1186/s40854-020-00205-1
Citazione non strutturata
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.
https://doi.org/10.1007/s40745-017-0122-3
Citazione non strutturata
Hill T., Lewicki P. (2006). Statistics. Methods and Applications, StatSoft, Tulsa. Hoover K.D. (2001). Causality in Macroeconomics,
Cambridge University Press, Cambridge.
https://doi.org/10.1016/B978-0-323-03707-5.50024-3
Citazione non strutturata
Jaquart P., Kopke S., Weinhardt C. (2022). Machine learning for cryptocurrency market prediction and trading, J. Financ. Data
Sci., 8: 331-352.
https://doi.org/10.1016/j.jfds.2022.12.001
Citazione non strutturata
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.
https://doi.org/10.1371/journal.pone.0177630
Citazione non strutturata
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.
https://doi.org/10.1016/j.intfin.2020.101188
Citazione non strutturata
Kufenko V., Geiger N. (2016). Business cycles in the economy and in economics: an econometric analysis, Scientometrics, 107:
43-69.
https://doi.org/10.1007/s11192-016-1866-9
Citazione non strutturata
Kwapień J., Wątorek M., Drożdż S. (2021). Cryptocurrency Market Consolidation in 2020-2021, Entropy, 23(12), 1674.
https://doi.org/10.3390/e23121674
Citazione non strutturata
Laskowski M., Kim H.M. (2016). Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence, arXiv,
1611.00315v1.
https://doi.org/10.2139/ssrn.2798486