  What is the role of social interactions in the creation of price bubbles?
Answering this question requires obtaining collective behavioural traces
generated by the activity of a large number of actors. Digital currencies offer
a unique possibility to measure socio-economic signals from such digital
traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has
experienced periods of rapid increase in exchange rates (price) followed by
sharp decline; we hypothesise that these fluctuations are largely driven by the
interplay between different social phenomena. We thus quantify four
socio-economic signals about Bitcoin from large data sets: price on on-line
exchanges, volume of word-of-mouth communication in on-line social media,
volume of information search, and user base growth. By using vector
autoregression, we identify two positive feedback loops that lead to price
bubbles in the absence of exogenous stimuli: one driven by word of mouth, and
the other by new Bitcoin adopters. We also observe that spikes in information
search, presumably linked to external events, precede drastic price declines.
Understanding the interplay between the socio-economic signals we measured can
lead to applications beyond cryptocurrencies to other phenomena which leave
digital footprints, such as on-line social network usage.
