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AIS - A Metric for Assessing the Impact of an Influencer’s Twitter Activity on the Price of a Cryptocurrency

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Cooperative Information Systems (CoopIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14353))

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Abstract

Individual users on social media platforms like Twitter can significantly volatile assets, including cryptocurrencies. However, current research has overlooked this aspect, focusing on sentiment analysis that includes all posts from all users. Making it challenging to detect trends caused by individuals. To address this gap, we introduce the Asset Influence Score (AIS), a percentage-based metric that assesses the likelihood of a newly issued tweet aligning with periods of heightened trading activity. By analyzing price data and tweets concurrently, we identify correlations that enable to predict the likelihood of specific users’ tweets co-occurring with increased trading activity. Evaluating the AIS using a publicly available prototype and Twitter data from 2020 to 2023, we find that using the AIS as a buy signal outperforms buy-and-hold and technical trading strategies while maintaining high liquidity. Demonstrating the applicability of AIS in improving trading decisions and identifying key individuals on social media platforms.

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Notes

  1. 1.

    https://git01lab.cs.univie.ac.at/university_research/masterarbeiten/ais.

  2. 2.

    https://min-api.cryptocompare.com/.

  3. 3.

    https://developer.twitter.com/en/docs/twitter-api.

  4. 4.

    https://www.binance.com/en.

  5. 5.

    https://cryptowat.ch/correlations.

  6. 6.

    https://cryptowat.ch/correlations.

  7. 7.

    https://git01lab.cs.univie.ac.at/university_research/masterarbeiten/ais.

  8. 8.

    https://git01lab.cs.univie.ac.at/university_research/masterarbeiten/ais/-/blob/main/eval/Doge_PDR_Vol_Momentum_Vol.pine.

  9. 9.

    https://git01lab.cs.univie.ac.at/university_research/masterarbeiten/ais/-/tree/main/eval.

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Authors and Affiliations

  1. Faculty of Computer Science, Research Group Software Architecture, University of Vienna, Währinger Straße 29, Vienna, Austria

    Kevin Miller & Kristof Böhmer

Authors
  1. Kevin Miller
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  2. Kristof Böhmer
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Correspondence to Kevin Miller .

Editor information

Editors and Affiliations

  1. Telecom SudParis, Evry, France

    Mohamed Sellami

  2. Leibniz University Hannover, Hannover, Germany

    Maria-Esther Vidal

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Boudewijn van Dongen

  4. Telecom SudParis, Evry, France

    Walid Gaaloul

  5. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

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Miller, K., Böhmer, K. (2024). AIS - A Metric for Assessing the Impact of an Influencer’s Twitter Activity on the Price of a Cryptocurrency. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-46846-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46845-2

  • Online ISBN: 978-3-031-46846-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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