Towards Explainable AI: Relationship Between Twitter Sentiment, User Behaviour, and Bitcoin Price Prediction

Bitcoin prices have been predicted using Twitter sentiments, with results showing relatively low prediction accuracy. Additional external data sources, such as Google Trends, have been used to improve prediction accuracy. However, to the best of our knowledge, no analytical approach has been used to explain why Twitter sentiment is not a good predictor and why additional external data improved predictive model accuracy. Consequently, this paper uses cluster analysis and Shapley Additive Explanations (SHAP) to analyse feature importance and impact on the prediction outcome of the eXtreme Gradient Boosting (XGBoost) model. A combination of Twitter sentiments and user interaction behaviour, such as likes, retweets, and replies, are used as input variables for the XGBoost model and Bitcoin closing prices as the target variable. Our findings indicate that the sentiment score is insufficient because the majority of Bitcoin-related tweets come from Bitcoin enthusiasts whose opinions are unaffected by market fluctuations, and the improved prediction accuracy observed when external data are used in addition to the sentiment score is significant only during price volatility and can be attributed to an increase in the total number of interactions from new sets of users and not the cumulative user behaviour.