Rasyidi Faiz Akbar
Purpose – This study aims to examine whether sentiment polarity or public attention conveys informational value for Bitcoin return and volatility dynamics by separating human-generated signals from automation-driven amplification. Design/methodology/approach – The analysis uses more than sixteen million Bitcoin-related tweets, classifies accounts into human-like and automation-like groups and constructs separate sentiment and attention indices. These indicators enter a multi-stage empirical framework comprising return-prediction models, GARCH-X volatility estimation and VAR-based return-attention dynamics across volatility regimes. Findings – Polarity-based sentiment, hype, anxiety and divergence exhibit no predictive power for returns across all specifications. Public attention, however, significantly amplifies conditional variance and improves GARCH-X model performance while offering no directional content for returns. VAR and Granger causality show that attention reacts to price shocks rather than forecasting them. Automation-like accounts dominate the dataset and dilute polarity signals, whereas attention remains robust as a behavioural intensity measure. Originality/value – The study demonstrates that attention, not textual polarity, drives short-horizon volatility in cryptocurrency markets and provides a refined empirical framework for modelling digitally mediated market behaviour. © 2026 Emerald Publishing Limited
Department of Management, Universitas Negeri Surabaya, Surabaya, Indonesia
