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Role of social networks and machine learning techniques in cryptocurrency price prediction: a survey

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Abstract

One of the most important and useful methods for analyzing extremely complex financial data, particularly cryptocurrencies, is machine learning. Due to the enormous volumes of social network data being produced, it is urgently necessary to use this data properly for cryptocurrency price prediction. This survey article includes a literature analysis for the investigation of several machine learning approaches utilized for many recent, highly regarded publications' predictions of the price of cryptocurrencies. We are able to examine the current machine learning and deep learning models being used for cryptocurrency prediction based on social networks by taking only the most recent research into account. This analysis of the literature notes a distinct shift in the artificial intelligence methods applied to the cryptocurrency industry, with particularly deep learning methods taking precedence over machine learning methods.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA, for funding this research work through the project number NBU-FFR-2024-2729-02.

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Authors and Affiliations

  1. Research in Software Engineering and Applied, Distributed and Intelligent Computer Science RIADI Labs, ENSI, University of Manouba, Manouba, Tunisia

    Ameni Youssfi Nouira

  2. University of Sousse, Sousse, Tunisia

    Mariam Bouchakwa

  3. Department of Computer Sciences, Faculty of Sciences, Northern Border University, 73222, Arar, Saudi Arabia

    Marwa Amara

  4. LARIA UR22ES01, ENSI, Manouba University, Manouba, Tunisia

    Marwa Amara

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  1. Ameni Youssfi Nouira
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Ameni Youssfi Nouira is the corresponding author Mariam Bouchakwa is contributing authors Marwa Amara is contributing authors.

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Nouira, A.Y., Bouchakwa, M. & Amara, M. Role of social networks and machine learning techniques in cryptocurrency price prediction: a survey. Soc. Netw. Anal. Min. 14, 152 (2024). https://doi.org/10.1007/s13278-024-01316-8

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  • DOI: https://doi.org/10.1007/s13278-024-01316-8

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