Quantifying the Herd: Social Media Sentiment, Leverage, and Bitcoin Market Volatility
Abstract
This study examines the impact of social media sentiment on Bit-coin market volatility. While existing literature often relies on single-source data or isolated factors, this research introduces a novel three-source pricing framework that integrates Twitter-derived social media sentiment, investor leverage ratios, and historical market data. Using a Weighted Least Squares (WLS) regression model to address heteroscedasticity in financial time series, we analyze daily Bitcoin returns from 2021 to the first half of 2022. Our results indicate that both social media sentiment has a statistically significant positive effect on Bitcoin returns. The model successfully identified high-risk market conditions, as validated by the May-June 2021 crash. These findings demonstrate that social media sentiment has a huge impact on cryptocurrency markets.
References
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Jung, H. S., Lee, H., & Kim, J. H. (2025). Detecting Bitcoin sentiment using language models. Neural Processing Letters, 57, 1–25. https://doi.org/10.1007/s11063-025-01234-5
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Koutmos, D. (2023). Investor sentiment and Bitcoin prices. Review of Quantitative Finance and Accounting, 60(1), 1–29. https://doi.org/10.1007/s11156-022-01045-7
Kraaijeveld, O., & De Smedt, J. (2020). Predictive power of Twitter sentiment for cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 65, 101188. https://doi.org/10.1016/j.intfin.2020.101188
Kreuzer, C., Sparrer, C., & Dorfleitner, G. (2026). News sentiment and BTC/ETH futures. Review of Derivatives Research, 29(1), 3–25.
Kutner, M. H., et al. (2005). Applied linear statistical models (5th ed.). McGraw-Hill.
Lamon, C., Nielsen, E., & Redondo, E. (2017). Cryptocurrency price prediction using sentiment. SMU Data Science Review, 1(3), 1–22.
Li, M., Manahov, V., & Ashton, J. (2025). Bitcoin price and sentiment: Evidence from crypto heist. North American Journal of Economics and Finance, 78, 102432. https://doi.org/10.1016/j.najef.2025.102432
López-Cabarcos, M. Á., et al. (2021). Bitcoin volatility and investor sentiment. Finance Research Letters, 38, 101399. https://doi.org/10.1016/j.frl.2020.101399
Mohapatra, S., Ahmed, N., & Alencar, P. (2019). KryptoOracle: Real-time prediction using Twitter sentiment. In IEEE Big Data Conference. https://doi.org/10.1109/BigData47090.2019.9006101
Mokni, K., Bouteska, A., & Nakhli, M. S. (2022). Investor sentiment and Bitcoin. North American Journal of Economics and Finance, 60, 101657. https://doi.org/10.1016/j.najef.2022.101657
Roozkhosh, P., & Pooya, A. (2024). Bitcoin price and sentiment dynamics. Computational Economics, 64(2), 1163–1198. https://doi.org/10.1007/s10614-023-10456-8
Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2025). Sentiment-driven cryptocurrency forecasting. Social Network Analysis and Mining, 15, 52. https://doi.org/10.1007/s13278-025-01025-3
Shiller, R. J. (2000). Irrational exuberance. Princeton University Press.
Smuts, N. (2019). What drives cryptocurrency prices? ACM SIGMETRICS, 46(3), 131–134.
Stenqvist, E., & Lönnö, J. (2017). Predicting Bitcoin price fluctuation using Twitter sentiment. Unpublished manuscript.
Tiwari, D., et al. (2025). Swarm optimization sentiment model for crypto prediction. Scientific Reports, 15, 8119. https://doi.org/10.1038/s41598-025-12345-6
Trimborn, S., & Härdle, W. K. (2018). CRIX index for cryptocurrencies. Journal of Empirical Finance, 49, 107–122. https://doi.org/10.1016/j.jempfin.2018.08.004
Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Cryptocurrency prediction using sentiment analysis. Entropy, 21(6), 589. https://doi.org/10.3390/e21060589
Yang, S. Y., et al. (2025). Cryptocurrency jump contagion and sentiment. European Journal of Finance, 1–19.
Yiqun, T. (2022). Investor sentiment and cryptocurrency prices under policy shocks. Shanghai University of Finance and Economics Thesis. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD202402&filename=1023425080.nh
Youvan, D. C. (2024). Understanding sentiment analysis with VADER. AI and Data Science Journal.
Aslam, N., et al. (2022). Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model. IEEE Access, 10, 39313–39324. https://doi.org/10.1109/ACCESS.2022.3165445
Barndorff-Nielsen, O. E., & Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B, 64(2), 253–280. https://doi.org/10.1111/1467-9868.00393
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. Wiley.
Ben Osman, M., et al. (2025). Does investor sentiment drive the Bitcoin price? Digital Finance, 1–28. https://doi.org/10.1007/s42521-025-00123-x
Bhatt, S., Ghazanfar, M., & Amirhosseini, M. (2023). Machine learning based cryptocurrency price prediction using historical data and social media sentiment. Computer Science & Information Technology, 13(10), 1–11.
BIRAU, F. R. (2012). Econometric approach of heteroskedasticity on financial time series in a general framework. Economy Series, 4, 74–77.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Prentice Hall.
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
Chen, Q. (2025). Sentiment-aware mean-variance portfolio optimization for cryptocurrencies. arXiv. https://arxiv.org/abs/2508.16378
CoinMetrics. (2024). CoinMetrics asset metrics documentation. https://docs.coinmetrics.io/
Critien, J. V., Gatt, A., & Ellul, J. (2022). Bitcoin price change and trend prediction through Twitter sentiment and data volume. Financial Innovation, 8(1), 1–20. https://doi.org/10.1186/s40854-022-00346-5
CryptoQuant. (2024). CryptoQuant API documentation data specifications. https://docs.cryptoquant.com/
Eom, C., et al. (2019). Bitcoin and investor sentiment: Statistical characteristics and predictability. Physica A, 514, 511–521. https://doi.org/10.1016/j.physa.2018.10.002
Farzulla, M. (2026). The extremity premium: Sentiment regimes and adverse selection in cryptocurrency markets. arXiv. https://arxiv.org/abs/2602.07018
Gaies, B., et al. (2021). Is Bitcoin rooted in confidence? Technological Forecasting and Social Change, 172, 121038. https://doi.org/10.1016/j.techfore.2021.121038
Gb, H. (2023). Cryptocurrency price prediction using Twitter sentiment analysis. arXiv. https://arxiv.org/abs/2303.09397
Georgoula, I., et al. (2015). Using time-series and sentiment analysis to detect determinants of Bitcoin prices. In Proceedings of conference.
Ghazouani, I., Ghazouani, I., & Omri, A. (2025). Virtual influence, real impact: Sentiment and cryptocurrency dynamics. Blockchain: Research and Applications, 100375. https://doi.org/10.1016/j.bcra.2025.100375
Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of Twitter sentiment analysis methods. ACM Computing Surveys, 49(2), 1–41. https://doi.org/10.1145/2932705
Girsang, A. S. (2023). Hybrid LSTM and GRU for cryptocurrency price forecasting. IEEE Access, 11, 120530–120540. https://doi.org/10.1109/ACCESS.2023.3267890
Glassnode. (2024). Glassnode data methodology API guide. https://docs.glassnode.com/
Hajek, P., Hikkerova, L., & Sahut, J. M. (2023). How well do investor sentiment and ensemble learning predict Bitcoin prices? Research in International Business and Finance, 64, 101836. https://doi.org/10.1016/j.ribaf.2023.101836
Han, B., Liu, H., & Sui, P. (2026). Social network and sentiment contagion: Evidence from Bitcoin market. Federal Reserve Bank of Dallas Working Paper, 2605.
Hoaglin, D. C., & Welsch, R. E. (1978). The hat matrix in regression and ANOVA. The American Statistician, 32(1), 17–22. https://doi.org/10.1080/00031305.1978.10479299
Htay, H. S., Ghahremani, M., & Shiaeles, S. (2025). Enhancing Bitcoin price prediction with deep learning. Applied Sciences, 15(3), 1554. https://doi.org/10.3390/app15031554
Huang, X., et al. (2021). LSTM based sentiment analysis for cryptocurrency prediction. In International Conference on Database Systems. Springer.
Hutto, C. J., & Gilbert, E. (2014). VADER sentiment analysis. GitHub Software. https://github.com/cjhutto/vaderSentiment
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
Isnan, M., Elwirehardja, G. N., & Pardamean, B. (2023). Sentiment analysis for TikTok review using VADER and SVM. Procedia Computer Science, 227, 168–175. https://doi.org/10.1016/j.procs.2023.10.028
Jung, H. S., Lee, H., & Kim, J. H. (2025). Detecting Bitcoin sentiment using language models. Neural Processing Letters, 57, 1–25. https://doi.org/10.1007/s11063-025-01234-5
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. ASME Journal of Basic Engineering, 82(1), 35–45.
Koutmos, D. (2023). Investor sentiment and Bitcoin prices. Review of Quantitative Finance and Accounting, 60(1), 1–29. https://doi.org/10.1007/s11156-022-01045-7
Kraaijeveld, O., & De Smedt, J. (2020). Predictive power of Twitter sentiment for cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 65, 101188. https://doi.org/10.1016/j.intfin.2020.101188
Kreuzer, C., Sparrer, C., & Dorfleitner, G. (2026). News sentiment and BTC/ETH futures. Review of Derivatives Research, 29(1), 3–25.
Kutner, M. H., et al. (2005). Applied linear statistical models (5th ed.). McGraw-Hill.
Lamon, C., Nielsen, E., & Redondo, E. (2017). Cryptocurrency price prediction using sentiment. SMU Data Science Review, 1(3), 1–22.
Li, M., Manahov, V., & Ashton, J. (2025). Bitcoin price and sentiment: Evidence from crypto heist. North American Journal of Economics and Finance, 78, 102432. https://doi.org/10.1016/j.najef.2025.102432
López-Cabarcos, M. Á., et al. (2021). Bitcoin volatility and investor sentiment. Finance Research Letters, 38, 101399. https://doi.org/10.1016/j.frl.2020.101399
Mohapatra, S., Ahmed, N., & Alencar, P. (2019). KryptoOracle: Real-time prediction using Twitter sentiment. In IEEE Big Data Conference. https://doi.org/10.1109/BigData47090.2019.9006101
Mokni, K., Bouteska, A., & Nakhli, M. S. (2022). Investor sentiment and Bitcoin. North American Journal of Economics and Finance, 60, 101657. https://doi.org/10.1016/j.najef.2022.101657
Roozkhosh, P., & Pooya, A. (2024). Bitcoin price and sentiment dynamics. Computational Economics, 64(2), 1163–1198. https://doi.org/10.1007/s10614-023-10456-8
Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2025). Sentiment-driven cryptocurrency forecasting. Social Network Analysis and Mining, 15, 52. https://doi.org/10.1007/s13278-025-01025-3
Shiller, R. J. (2000). Irrational exuberance. Princeton University Press.
Smuts, N. (2019). What drives cryptocurrency prices? ACM SIGMETRICS, 46(3), 131–134.
Stenqvist, E., & Lönnö, J. (2017). Predicting Bitcoin price fluctuation using Twitter sentiment. Unpublished manuscript.
Tiwari, D., et al. (2025). Swarm optimization sentiment model for crypto prediction. Scientific Reports, 15, 8119. https://doi.org/10.1038/s41598-025-12345-6
Trimborn, S., & Härdle, W. K. (2018). CRIX index for cryptocurrencies. Journal of Empirical Finance, 49, 107–122. https://doi.org/10.1016/j.jempfin.2018.08.004
Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Cryptocurrency prediction using sentiment analysis. Entropy, 21(6), 589. https://doi.org/10.3390/e21060589
Yang, S. Y., et al. (2025). Cryptocurrency jump contagion and sentiment. European Journal of Finance, 1–19.
Yiqun, T. (2022). Investor sentiment and cryptocurrency prices under policy shocks. Shanghai University of Finance and Economics Thesis. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD202402&filename=1023425080.nh
Youvan, D. C. (2024). Understanding sentiment analysis with VADER. AI and Data Science Journal.
Published
2026-06-30
How to Cite
YUAN TOM, Liu Hong et al.
Quantifying the Herd: Social Media Sentiment, Leverage, and Bitcoin Market Volatility.
Theoretical and Practical Research in Economic Fields, [S.l.], v. 17, n. 2, p. 509 - 527, june 2026.
ISSN 2068-7710.
Available at: <https://journals.aserspublishing.eu/tpref/article/view/9522>. Date accessed: 02 july 2026.
doi: https://doi.org/10.14505/tpref.v17.2(38).15.
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