Abstract
This paper presents a preliminary analysis of cryptocurrency brands on Twitter, carried out through the Semantic Brand Score Brand Intelligence App (a tool hosted in the ENEAGRID digital infrastructure). The aim is to rank five digital coins (i.e. Bitcoin, Ethereum, Zcash, Monero, Litecoin). Web crawling, data storage and brand scoring activities require computational power. The ENEAGRID infrastructure faces this challenge in terms of computational costs, with its computing core represented by the HPC CRESCO clusters. In our methodology, we run periodic sessions of web crawling to create a database of tweets (concerning digital coins), then we use the Semantic Brand Score to evaluate cryptocurrencies relevance and study their brand image. This case study is a first step towards collaborating with experts and research communities in financial domain and opening access to ENEA Virtual Labs.
Keywords
- Social network analysis
- Semantic brand score
- Cryptocurrency
- Financial text mining
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References
Aaker, D.A.: Measuring brand equity across products and markets. Calif. Manag. Rev. 38(3), 102–120 (1996)
Baker, S.R., Bloom, N., Davis, S.J.: Measuring economic policy uncertainty. Q. J. Econ. 131(4), 1593–1636 (2016). https://doi.org/10.1093/qje/qjw024. https://academic.oup.com/qje/article/131/4/1593/2468873
Boldi, P., et al.: BUbiNG: Massive Crawling for the Masses (2016)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011). https://doi.org/10.1016/j.jocs.2010.12.007
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979). https://doi.org/10.1016/0378-8733(78)90021-7
Fronzetti Colladon, A., Grippa, F.: Brand Intelligence analytics. In: Przegalinska, A., Grippa, F., Gloor, P.A. (eds.) COINs 2019. SPC, pp. 125–141. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48993-9_10
Fronzetti Colladon, A.: The semantic brand score. J. Bus. Res. 88, 150–160 (2018). https://doi.org/10.1016/j.jbusres.2018.03.026
Fronzetti Colladon, A.: Forecasting election results by studying brand importance in online news. Int. J. Forecast. 36(2), 414–427 (2020)
Colladon, A.F., Grippa, F., Innarella, R.: Studying the association of online brand importance with museum visitors: an application of the semantic brand score. Tour. Manag. Perspect. 33, 100588 (2020). https://doi.org/10.1016/j.tmp.2019.100588
Fronzetti Colladon, A., Naldi, M.: Distinctiveness centrality in social networks. PLOS ONE 15(5), e0233276 (2020). https://doi.org/10.1371/journal.pone.0233276
Keller, K.L.: Conceptualizing, measuring, and managing customer-based brand equity. J. Mark. 57(1), 1–22 (1993)
Yen, K.-C., Cheng, H.-P.: Economic policy uncertainty and cryptocurrency volatility. https://www.sciencedirect.com/science/article/abs/pii/S1544612319310189
Matta, M., Lunesu, I., Marchesi, M.: Bitcoin spread prediction using social and web search media. In: Conference: Workshop Deep Content Analytics Techniques for Personalized and Intelligent Services (2015)
Mariano, A., Adani, M., Briganti, G., Mircea, M.: Qualità dell’aria: Algoritmi di Machine Learning applicati a dati simulati dal sistema modellistico AMS-MINNI. In: Conference on GARR ‘Data Revolution’, pp. 75–81 (2018). https://doi.org/10.26314/garr-conf2018-proceedings-14
Migliori, S., et al.: CRESCO HPC Clusters Evolution, Users and Workloads 2008–2018 - in High Performance Computing on CRESCO Infrastructure: Research Activity Results 2018 (2019). https://ict.enea.it/wp-content/uploads/2020/02/report_cresco_2018_for_web.pdf. ISBN: 978-88-8286-390-6
Ponti, G., et al.: The role of medium size facilities in the HPC ecosystem: the case of the new CRESCO4 cluster integrated in the ENEAGRID infrastructure. In: Proceedings of the 2014 International Conference on High Performance Computing and Simulation, HPCS 2014, Art. no. 6903807, pp. 1030–1033 (2014)
Santomauro, G., Alderuccio, D., Ambrosino, F., Fronzetti Colladon, A., Migliori, S.: A brand scoring system for cryptocurrencies based on social media data. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds.) MIDAS 2019. LNCS (LNAI), vol. 11985, pp. 127–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37720-5_11
Steinert, L., Herff, C.: Predicting altcoin returns using social media. PLoS ONE 13(12), e0208119 (2018). https://doi.org/10.1371/journal.pone.0208119
Acknowledgements
The authors are grateful to Andrea Fronzetti Colladon, professor at the University of Perugia, who has been collaborating with their team at ENEA for more than two years and who provided technical and theoretical support for the writing of this paper. The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff Ponti et al. (2014). CRESCO/ENEAGRID HPC infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes, see http://www.cresco.enea.it/english for information.
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Santomauro, G., Alderuccio, D., Ambrosino, F., Migliori, S. (2021). Ranking Cryptocurrencies by Brand Importance: A Social Media Analysis in ENEAGRID. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Ponti, G., Severini, L. (eds) Mining Data for Financial Applications. MIDAS 2020. Lecture Notes in Computer Science(), vol 12591. Springer, Cham. https://doi.org/10.1007/978-3-030-66981-2_8
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