  Social media signals have been successfully used to develop large-scale
predictive and anticipatory analytics. For example, forecasting stock market
prices and influenza outbreaks. Recently, social data has been explored to
forecast price fluctuations of cryptocurrencies, which are a novel disruptive
technology with significant political and economic implications. In this paper
we leverage and contrast the predictive power of social signals, specifically
user behavior and communication patterns, from multiple social platforms GitHub
and Reddit to forecast prices for three cyptocurrencies with high developer and
community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance
of neural network models that rely on long short-term memory units (LSTMs)
trained on historical price data and social data against price only LSTMs and
baseline autoregressive integrated moving average (ARIMA) models, commonly used
to predict stock prices. Our results not only demonstrate that social signals
reduce error when forecasting daily coin price, but also show that the language
used in comments within the official communities on Reddit (r/Bitcoin,
r/Ethereum, and r/Monero) are the best predictors overall. We observe that
models are more accurate in forecasting price one day ahead for Bitcoin (4%
root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
