A Panel Data Set of Cryptocurrency Development Activity on GitHub

Tonder, Rijnard van; Trockman, Asher; Goues, Claire Le

doi:10.1109/msr.2019.00037

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Next, Trockman et al [14] investigated the relationship between active communities of developers and the price of some cryptocurrencies. The authors did not identify any compelling evidence for causality between development activity metrics and market cap [13].…”

Section: Bitcoin Price Formation Modelsmentioning

confidence: 85%

“…Total amount of activity on GitHub [13], [14] Miners' Activity Total amount of computing power [12], [32] Proliferation of End Users Number of end users [19], [38]- [40]…”

Section: Developers' Activitymentioning

confidence: 99%

“…Inspired by the work of van Tonder et al [13], we developed a customized script with Python and downloaded the number of daily commits from the Bitcoin Core page on GitHub.…”

Section: Data Collectionmentioning

confidence: 99%

“…Subsequent researchers (e.g., [12]) developed models focused on other aspects, such as the miners' activities. Further, some researchers (e.g., [13], [14]) introduced the idea of correlating the developers' activity with the price of a cryptocurrency. All of these studies have a common line of reasoning: they study cryptocurrencies from the price perspective, with the assumption that price is the only indicator of success.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Proliferation of End Users as a Success Metric for Cryptocurrencies

Rossi¹,

Avital²,

Gleasure³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Over the last decade, numerous studies have examined the remarkable appreciation of cryptocurrencies and have typically focused on their price and the factors that predict them. In contrast, this paper argues that the success of a cryptocurrency is determined not only by its monetary value but also by the proliferation of its end users. Specifically, we hypothesize that changes in developers' and miners' activities drive the growing proliferation of a cryptocurrency's end users. Building on the Bitcoin case, we use a time-series model based on 4,285 Bitcoin daily observations to suggest that changes in the number of end users are anticipated by surges or drops in activity by the developers and miners who develop and maintain the network. We further find a limited relationship between these variables and the price of Bitcoin. These results support an alternative view of cryptocurrencies' success and highlight further research avenues in this nascent domain.

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Section: Bitcoin Price Formation Modelsmentioning

confidence: 85%

“…Total amount of activity on GitHub [13], [14] Miners' Activity Total amount of computing power [12], [32] Proliferation of End Users Number of end users [19], [38]- [40]…”

Section: Developers' Activitymentioning

confidence: 99%

“…Inspired by the work of van Tonder et al [13], we developed a customized script with Python and downloaded the number of daily commits from the Bitcoin Core page on GitHub.…”

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Proliferation of End Users as a Success Metric for Cryptocurrencies

Rossi¹,

Avital²,

Gleasure³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

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Metaverse in Investment Using Sentiment Analysis and Machine Learning

Yeoh

Chung

Wang

2023

Strategies and Opportunities for Technology in the Metaverse World

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At the end of 2019, individuals' outdoor activities were restricted due to the emergence of COVID-19. As a result of this phenomenon, interest in online activities and interaction in the metaverse environment has increased. Online games have exploded in popularity with the young generation in Metaverse where they can earn money through the platforms. Thus, it is desirable to investigate emerging technology and analyse how to invest using techniques, such as sentiment analysis and machine learning (ML), to predict crypto trends. This study analysed time series data for crypto price and text, where information like news, articles, and feedback from social media can use the input to generate the sentiment score to understand the crypto trends. FinBERT is a sentiment model that was used for this study to generate the result. The AI investing framework is built to incorporate both sentiment analysis technique and predictive model for this chapter, to address the research questions and enable one to make more informed decisions.

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A Panel Data Set of Cryptocurrency Development Activity on GitHub

Cited by 2 publications

References 13 publications

The Proliferation of End Users as a Success Metric for Cryptocurrencies

The Proliferation of End Users as a Success Metric for Cryptocurrencies

Metaverse in Investment Using Sentiment Analysis and Machine Learning

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