Does Social Media Sentiment Predict Bitcoin Trading Volume?

Abstract

Social media sentiment is proven to be an important feature in financial forecasting. While the effect of sentiment is complex and time-varying for traditional financial assets, its role in cryptocurrency markets is unclear. This research explores the predictive power of public sentiment on Bitcoin trading volume. We develop a novel sentiment analysis pipeline for processing Bitcoin-related tweets and achieve state-of-the-art accuracy on a benchmark dataset. Our pipeline also leverages information gain theory to incorporate the impact of textual and non-textual features. We use such features to discern a nonlinear relationship between public sentiment and Bitcoin trading volume and discover the optimal predictive horizon for Bitcoin. This research provides a useful module and a foundation for future studies and understanding of Bitcoin market dynamics, and its interaction with social media buzzing. © 2022 International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation". All Rights Reserved.