The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how “healthy” and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it.In this paper, we investigate whether developers’ emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers’ comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers’ comments over time, and test the corresponding time series (i.e. the affect time series) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers’ emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)—associated with the prediction of the prices of cryptocurrencies—significantly decreases when including the affect time series.

中文翻译：

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蝴蝶的“影响”：开发实践对加密货币价格的影响

分布式分类帐和区块链开源项目中的开发人员网络对于维护平台至关重要：了解其交易所的结构，分析其活动及其质量（例如，问题解决时间，评论中的礼貌）对于确定“健康”状况非常重要。高效的项目。项目的质量会影响对平台的信任度，从而影响到平台上交换的数字代币的价值。本文研究了开发人员的情绪是否可以有效地提供见解，从而可以改善对代币价格的预测。我们考虑了从Github提取的两个主要区块链项目（以太坊和比特币）的开发者评论和活动。随着时间的推移，我们会评估开发人员评论的情感和情绪（欢乐，爱，愤怒等），影响时间序列）与比特币/以太坊价格时间序列的相关性和因果关系。我们的分析表明，开发人员情绪时间序列与比特币/以太坊价格之间存在格兰杰因果关系。此外，使用人工递归神经网络（LSTM），我们可以证明，与影响加密货币价格有关的均方根误差（RMSE）显着降低。