YouTube-Driven Investor Sentiment for Bitcoin Price Prediction

Abstract

The rapid expansion of cryptocurrency markets has coincided with the growing prominence of social media platforms as influential channels for shaping investor sentiment. Among these platforms, YouTube has become a medium for disseminating investment opinions and behavioral signals. This study investigates the extent to which YouTube-derived features—such as video influence scores, sentiment embedded in video titles, and user engagement indicators—can enhance the prediction of Bitcoin price fluctuations. A novel dataset is compiled, covering the period from January 2015 to September 2025. Sentiment is assessed using a combination of transformer-based language models, while influence metrics are computed through engagement statistics adjusted for temporal decay and relevance. These features are combined with historical Bitcoin price data and applied within an XGBoost forecasting framework. The empirical findings suggest that augmenting price-based models with YouTube-related sentiment and engagement features yields a notable improvement in directional forecasting accuracy, outperforming price-only benchmarks by approximately 4 %. Moreover, the study highlights that YouTube-derived behavioral signals offer predictive insights that are not fully captured by conventional indicators such as Google Trends or the Crypto Fear and Greed Index.