Quote from: gmg on May 27, 2015, 05:51:40 AM

Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

See http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2607167

Abstract:
This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the-art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error-correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor's 500 stock market index (which indicates the general state of the global economy).

Number of Pages in PDF File: 14

Keywords: Bitcoins, error correction, machine learning, sentiment analysis

I'm sure google trends would also work. I would also assume that Twitter and other markers are lagging indicators, i.e. they pick up just as or immediately after the price rises.

During the last massive rise, the news were all over bitcoin and searches skyrocketed. I don't think the internet chat increased the price, rather the price change caused lots of internet chat.