Social media has played an important role in driving the narrative around cryptocurrency sector in recent years. Although initial paper of Satoshi Nakamoto was published on forum posts, see e.g. satoshi nakamoto posts, in the later years the hype about cryptocurrencies was nevertheless substantially driven on social media, especially twitter.
It is interesting that in recent times the social media became important also for the stock market sector, where subreddits like https://www.reddit.com/r/stocks/ have been important drivers of stocks like it happened with Gamestop earlier this year. Social media is increasingly democratizing the information of crowds, solving one of the earlier pain points of finance – namely how to inform people about the financial stocks. Though one would also strongly advise that the new investors pay a lot of attention to fundamental data about stocks.
But back to crypto social media analysis. How does one approach this?
First is to built a bot, which regularly analyses twitter, reddit, youtube and other social media websites. When analysing given text, one parses it to find mentions of cryptocurrency tickers and names, e.g. BTC and Bitcoin. Python library flashtext: https://github.com/vi3k6i5/flashtext
Here is an example of news title (from sentiment api), that have been tagged with respective cryptocurrencies:
Then, the text is classified in terms of sentiment. One way to build a classifier is for example by using Support Vector Machines for this purpose.
Both types of data gives us an effective way of crypto social media analysis – it allows us to display information both about the number of social media mentions of cryptocurrencies as well as about their sentiment.
The interesting thing is that the social media mentions often closely follow price, here is an example for Bitcoin:
In the last few days the relation was almost 1:1. It is thus useful to take crypto social media analysis as additional source of information into account when analysing the crypto market.