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Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance

Author

Listed:
  • Sejung Park

    (John Carroll University)

  • Han Woo Park

    (YeungNam University)

Abstract

This study investigates the popularity, global and local presence of the top cryptocurrency websites in terms of market capitalization, and geographic distribution of cryptocurrency information on the web by mapping the information network of cryptocurrency sites. The study also explores whether web traffic and social network metrics serve as valuable indicator of financial performance of cryptocurrencies. Both citation and social network analyses were employed. The results suggest that bitcoin.org, bitcoin.com and steemit.com are the most popular sites based on the number of hit counts. Information production and consumption about coins are concentrated in European and Asia–Pacific regions. Germany and technology startup companies are the most central sources for cryptocurrency-related information in the network. This study also found positive cross correlation outcomes between web traffic, social network attributes, and cryptocurrency performance indicators. The finding demonstrates that the number of TLDs and multiple social network centrality measures are useful indicators of cryptocurrency market capitalization, trading volume, and price.

Suggested Citation

  • Sejung Park & Han Woo Park, 2020. "Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 297-314, February.
    • Handle: RePEc:spr:qualqt:v:54:y:2020:i:1:d:10.1007_s11135-019-00840-6
    DOI: 10.1007/s11135-019-00840-6
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    References listed on IDEAS

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    Cited by:

    1. Ante, Lennart, 2023. "How Elon Musk's Twitter activity moves cryptocurrency markets," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
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    3. Klender Cortez & Martha del Pilar Rodríguez-García & Samuel Mongrut, 2020. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    4. Cao, Guangxi & Ling, Meijun, 2022. "Asymmetry and conduction direction of the interdependent structure between cryptocurrency and US dollar, renminbi, and gold markets," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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