Human signals, automated noise: identifying the true drivers of crypto volatility

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.