These protocols also mask the identities of cryptocurrency users, making transactions and fund flows difficult to attribute to specific individuals or groups.
Subscriber growth is harder to track than the other metrics. Only the current subscriber count is displayed for a particular subreddit, and historical data cannot be rebuilt retrospectively as subscribers do not have a visible historical imprint. A third-party website, RedditMetrics (http://redditmetrics.com/), has been retrieving and storing real-time subscriber counts; however their data on the particular subreddits of interest only reaches back to 2012 so the subscriber growth analysis can only start at this point. Fig 2 shows the three social media metric time series for each cryptocurrency; note that subscriber growth is the only metric that can have negative associated values, caused by more users unsubscribing than subscribing on a particular day.

For the sake of clarity an explicit definition of short, medium, and long term is required. In this work short term refers to the 2–4 and 4–8 day period bands. Medium term refers to the 8–16 and 16–32 day bands. Long term will be used to refer to the 32–64, 64–128, 128–256 and 256–512 day bands. The short, medium, and long term bands will be considered separately to begin with, and then considered collectively alongside the results of the GSADF bubble test.
In the case of cryptocurrencies, academia has barely scratched the surface with respect to identifying the determinants of their prices. For example, studies by Cheah and Fry (2015) and Corbet et al. (2018) claim that Bitcoin has no intrinsic value and that its price has persistently exhibited ‘bubble-like’ behaviour. Makarov and Schoar (2018) find that the prices of Bitcoin, Ethereum, and Ripple differ across exchanges for weeks. Outside of academia, the President of the United States recently tweeted that cryptocurrencies are based on “thin air”.1