Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data

TL;DR

This paper investigates how Twitter sentiment and user metadata influence cryptocurrency prices, using regression and LSTM models to analyze social media activity and predict market fluctuations.

Contribution

It introduces a comprehensive analysis combining tweet sentiment, user metadata, and advanced models to predict cryptocurrency prices, highlighting social media's impact on market volatility.

Findings

Twitter sentiment significantly correlates with crypto price fluctuations.

Metadata features improve prediction accuracy.

LSTM models outperform traditional regression methods.

Abstract

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 payments because it facilitates a very fast, easy and secure way of transactions. However, it is very volatile and is influenced by a range of factors, with social media being a major one. Thus, with over four billion active users of 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 it influences their prices. In addition, we also collect metadata related to tweets and users. We use all these features to also predict the price of cryptocurrency for which we use some regression-based models and an LSTM-based model.