The Influence Of Social Media Sentiment On Cryptocurrency Price Movements
Keywords:
Social media sentiment; Cryptocurrency; Bitcoin; investor attention; VAR; Granger causalityAbstract
Cryptocurrency markets are dominated by retail investors who exchange information and opinions on social media, raising the question of whether online sentiment drives prices or merely reflects them. This study examines the relationship between social media sentiment and the price movements of Bitcoin (BTC) and Ethereum (ETH) using daily data from January 2022 to June 2026 (1,640 observations per coin). Sentiment is measured as the daily average VADER compound score of posts on X (Twitter) and Reddit mentioning each coin, and attention as the daily change in post volume. The analysis employs vector autoregression (VAR), Granger causality tests, impulse response functions, variance decomposition, and predictive regressions with asymmetric sentiment effects. The results show a strong contemporaneous correlation between sentiment and returns (0.46 for BTC and 0.56 for ETH), but the dominant direction of causality runs from prices to sentiment: returns explain 54–69% of the forecast error variance of sentiment, whereas sentiment explains less than 1% of return variance. Lagged sentiment has a statistically significant but economically small predictive effect on returns, which is stronger for ETH and driven mainly by negative sentiment. Abnormal increases in post volume predict higher volatility the next day rather than the direction of prices. The findings indicate that social media sentiment is largely a reflection of market movements, with a limited but asymmetric feedback effect, and that attention matters more for risk than for returns.
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