Abstract
Bitcoin is based on peer-to-peer technology where there is no need for central authority or banks as intermediaries. The network carries out Bitcoin issuing and transaction management. It is open-source, decentralized by design, nobody owns it or controls Bitcoin, and everyone can participate. Bitcoin challenges the previous payment system through its unique properties and allows compelling use cases that were not feasible in the past. Due to its decentralized properties and lack of price control, many a time, celebrities can influence its value using their social media fan followership. Twitter is becoming common place for celebrities to share their sentiments about Bitcoin. Elon Musk, a juggernaut entrepreneur of this century, is a prominent celebrity who has had a significant influence on Bitcoin and has been instrumental in promoting and criticizing Bitcoin in past months. In this paper, we analyze how Elon Musk’s tweets affect Bitcoin prices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
What is Twitter and why should you use it? (2021) https://esrc.ukri.org/research/impact-toolkit/social-media/twitter/what-is-twitter. Accessed 5 Aug 2021
https://bitcoin.org/en. Accessed 5 Aug 2021
Top 5 Celebrities Who Love Crypto (2021) https://www.coolwallet.io/celebrities-who-love-cryptocurrency-bitcoin-and-dogecoin. Accessed 5 Aug 2021
Vasilev I, Slater D, Spacagna G, Roelants P, Zocca V (2019) Python deep learning: exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow. Packt Publishing Ltd
Amjad M, Shah D (2017) Trading bitcoin and online time series prediction. In: NIPS 2016 time series workshop
Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system
Garcia D, Schweitzer F (2015) Social signals and algorithmic trading of Bitcoin. R Soc Open Sci 2(9)
Chen R, Lazer M (2011) Sentiment analysis of twitter feed for the prediction of stock market movement. Stanford Comput Sci 229:15
Go A, Huang L, Bhayani R (2009) Twitter sentiment classification using distant supervision. Stanford Comput Sci
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up: sentiment classification using machine learning techniques. In: ACL-02 Conference on empirical methods in natural language processing, Philadelphia, PA, USA
Dixon M, Klabjan D, Bang JH (2017) Classification-based financial markets prediction using deep neural networks. ArXiv
McNally S, Roche J, Caton S (2018) Predicting the price of Bitcoin using machine learning. In: 26th Euromicro international conference on parallel, distributed and network-based processing (PDP)
Daniela M, Butoi A (2013) Data mining on Romanian stock market using neural networks for price prediction. Informatica Econ 17(3)
Shah D, Zhang K (2015) Bayesian regression and Bitcoin. In: 52nd annual Allerton conference on communication, control, and computing (Allerton)
Jang H, Lee J (2017) An empirical study on modelling and prediction of Bitcoin Prices with Bayesian neural networks based on Blockchain information. IEEE Early Access Articles
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, R.R., Arya, R.K., Kumar, J., Gururani, A., Dugh, R., Dugh, A. (2022). The Impact of Elon Musk Tweets on Bitcoin Price. In: Mandal, J.K., Hsiung, PA., Sankar Dhar, R. (eds) Topical Drifts in Intelligent Computing. ICCTA 2021. Lecture Notes in Networks and Systems, vol 426. Springer, Singapore. https://doi.org/10.1007/978-981-19-0745-6_44
Download citation
DOI: https://doi.org/10.1007/978-981-19-0745-6_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0744-9
Online ISBN: 978-981-19-0745-6
eBook Packages: EngineeringEngineering (R0)