The Future of Cryptocurrency Market Analysis: Social Media Data and User Meta-Data

• Published in: Lobachevskii journal of mathematics Vol. 45; no. 3; pp. 1160 - 1174
• Main Authors: Jain, Samyak, Johari, Sarthak, Delhibabu, Radhakrishnan
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.03.2024; Springer Nature B.V
• Subjects: Algebra; Analysis; Data analysis; Digital currencies; Digital media; Geometry; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Probability Theory and Stochastic Processes; Regression models; Social networks; cryptocurrency; mean absolute error; LSTM; maximum percentage error; SGD regressor; linear regression; random forest regressor; principal component analysis; root mean squared error; tweet sentiment data
• ISSN: 1995-0802; 1818-9962
• DOI: 10.1134/S1995080224600717

Cryptocurrency is a form of digital currency using cryptographic techniques in a decentralized system for secure peer-to-peer transactions. It is gaining much popularity over traditional methods of payment because it facilitates very fast, easy, and secure transactions. Social media is a significant influence, but it is also very volatile and subject to a variety of other factors. Thus, with over four billion active users on social media, we need to understand its influence on the crypto market and how it can lead to fluctuations in the values of these cryptocurrencies. In our work, we analyze the influence of activities on Twitter, in particular the sentiments of the tweets posted regarding cryptocurrencies and how they influence their prices. In addition, we also collect metadata related to tweets and users. We try to leverage these features to predict the price of cryptocurrency, for which we use some regression-based models and an LSTM-based model.