Social Cryptolytics Pipeline
MICHAEL RUNYAN, MATTHEW POOSER, and MICHAEL E. COTTERELL, University of Georgia, USA
CSCI 4960 & CSCI 6950 Summer 2020
There is a mass amount of social media information posted everyday and it can be valuable information, however much of it consists of noise. This investigation further the field of social cryptolytics, by proposing the previous price and sentiment of clusters of post based on readability and intelligence can be used to accurately predicted the fluctuation of Bitcoin price. Bitcoin was chosen as the currency of investigation for the large quantity and long history of posts and discussions, and the long history of price data. The social media outlet includes submissions from the Bitcoin subreddit, a leading medium of Bitcoin pertaining social media posts. This study proposes, the price fluctuation of Bitcoin can be defined through autoregression of the price and sentiment of social media posts after they been screened for noise using clustering techniques.