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Fan tokens are a form of cryptocurrency that allow owners to participate in various fan-related experiences such as voting on the music to be played during half-time breaks in sporting events. Since 2020, many elite sport teams have issued fan tokens, allegedly as a way to engage with fans and hear their voice. However, fan tokens also raise some concerns. They are largely gamified digital items that intend to keep fans within the providers’ app. Also, they can be traded in exchange platforms, which arguably transform them into collectibles, whose value can vary over time. Here, we explore fan tokens through a case study from a football (soccer) club (i.e., an F.C. Barcelona fan token). Drawing on literature from situational and structural characteristics of gambling, we analyse the gambling-like features that fan tokens include in their product design. Such features are discussed from a public health perspective, comparing what they mean in gambling contexts and how potentially harmful they could be for fan token holders.
Fan tokens are a form of cryptocurrency that allow owners to participate in various fan-related experiences such as voting on the music to be played during half-time breaks in sporting events. Since 2020, many elite sport teams have issued fan tokens, allegedly as a way to engage with fans and hear their voice. However, fan tokens also raise some concerns. They are largely gamified digital items that intend to keep fans within the providers’ app. Also, they can be traded in exchange platforms, which arguably transform them into collectibles, whose value can vary over time. Here, we explore fan tokens through a case study from a football (soccer) club (i.e., an F.C. Barcelona fan token). Drawing on literature from situational and structural characteristics of gambling, we analyse the gambling-like features that fan tokens include in their product design. Such features are discussed from a public health perspective, comparing what they mean in gambling contexts and how potentially harmful they could be for fan token holders.
Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values.
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