Consumer Attention, Sentiment Asymmetry, and Cryptocurrency Payment Adoption

Introduction: Cryptocurrency is heavily traded, but its use as a routine payment instrument is still uneven and difficult to observe with public data. This study examines whether search attention and social-media sentiment can provide early signals of cryptocurrency payment-adoption momentum. Methods: A reproducible monthly proxy panel for January 2019-June 2025 is built from payment-specific search attention, lexicon-based social-media sentiment, a normalised on-chain activity proxy, and a broad crypto-market sentiment control. The analysis uses first differences, one- to four-month Granger-predictability tests, a reduced-form VAR model, and sentiment-regime comparisons. Results: Search attention contains stable predictive information for later payment activity. In the pilot panel, the null that search attention does not Granger-predict transaction activity is rejected at all four lags, including lag 1 (F = 9.08, p = 0.004). Sentiment is less convincing as a standalone linear predictor, but it is informative as a downside-risk regime. Mean payment activity is much lower during negative sentiment months than during positive months (95.85 versus 103.39), while positive and neutral months are relatively close. Conclusions: The results suggest that search attention helps identify adoption windows, whereas negative sentiment works mainly as a trust brake. The contribution is a transparent, public-data-compatible workflow for monitoring digital-asset payment adoption. Because the panel is a proxy rather than merchant-level payment evidence, the findings should be read as diagnostic and predictive rather than causal.