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Sentiment Matters for Cryptocurrencies: Evidence from Tweets

Author

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  • Radu Lupu

    (Department of International Business and Economics, The Bucharest University of Economic Studies, 010374 București, Romania)

  • Paul Cristian Donoiu

    (Department of International Business and Economics, The Bucharest University of Economic Studies, 010374 București, Romania)

Abstract

This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. We classify sentiment into positive, negative, and neutral categories and analyze its effects on log returns, liquidity, and price jumps by examining market reactions around tweet occurrences. Our findings show that tweets significantly impact trading volume and liquidity: neutral sentiment tweets enhance liquidity consistently, negative sentiments prompt immediate volatility spikes, and positive sentiments exert a delayed yet lasting influence on the market. This highlights the critical role of social media sentiment in influencing intraday market dynamics and extends the research on sentiment-driven market efficiency.

Suggested Citation

  • Radu Lupu & Paul Cristian Donoiu, 2025. "Sentiment Matters for Cryptocurrencies: Evidence from Tweets," Data, MDPI, vol. 10(4), pages 1-13, April.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:4:p:50-:d:1626154
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    References listed on IDEAS

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    1. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Pawe{l} O'swik{e}cimka & Tomasz Stanisz & Marcin Wk{a}torek, 2020. "Complexity in economic and social systems: cryptocurrency market at around COVID-19," Papers 2009.10030, arXiv.org.
    2. Pedersen, Lasse Heje, 2022. "Game on: Social networks and markets," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1097-1119.
    3. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    4. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    5. Goyenko, Ruslan Y. & Holden, Craig W. & Trzcinka, Charles A., 2009. "Do liquidity measures measure liquidity?," Journal of Financial Economics, Elsevier, vol. 92(2), pages 153-181, May.
    6. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    Cited by:

    1. Qizhao Chen, 2025. "Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies," Papers 2508.16378, arXiv.org.

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