Money Often Costs Too Much: A Study to Investigate The Effect Of Twitter Sentiment On Bitcoin Price Fluctuation

Abstract

Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.

The price and cost of bitcoin

“…Positive, but price may lead interest Ciaian et al, 2016;Glaser et al, 2014;Kristoufek, 2013;Kristoufek, 2015); Yermack, 2013 Tweet volume Twitter sentiment Social media sentiment and word of mouth (Reddit posts and new subscribers, Google search volume, Wikipedia views, Facebook shares) Tweets; forum posts Positive Positive (Garcia et al, 2014) Positive (Pant et al, 2018;Garcia et al, 2014;Garcia et al, 2015;Phillips & Gorse, 2017) Insignificant (Verma & Sharma, 2020) Positive (Mai et al, 2018;Kim et al, 2016;Phillips & Gorse, 2017;Ciaian et al, 2016;Kristoufek, 2013, Pant et al, 2018Yermack, 2013) Autoregressive models Useful (Azari, 2019;Garcia et al, 2014;Chevapatrakul & Mascia, 2019) Despite the many models that have been used to investigate bitcoin's price movements, there is little agreement as to what factors are most important and, in some cases, whether certain factors have positive or negative impacts on bitcoin's price. Attempts to resolve these differences have included separating bitcoin's history into two or more periods (Ciaian, 2016;Li & Wang, 2017), distinguishing between short-term and long-term impacts Ciaian et al, 2016;Kristoufek, 2015;Li & Wang, 2017), and considering nonlinear formulations (Balcilar et al, 2017).…”