The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity

• Published in: Big data and cognitive computing Vol. 6; no. 2; p. 59
• Main Authors: Trigka, Maria, Kanavos, Andreas, Dritsas, Elias, Vonitsanos, Gerasimos, Mylonas, Phivos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Audiences; Blockchain; Correlation coefficients; cryptocurrency; Cryptography; Currency; Digital currencies; Internet access; Kendall Correlation Coefficient; Pearson Correlation Coefficient; Predictions; Sentiment analysis; social media analytics; Social networks; Time series
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc6020059

Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity.