As the volume of data from online social networks increases, sci- entists are trying to find ways to understand and extract knowledge from this data. In this paper we study how the activity in a popular micro-blogging platform (Twitter) is correlated to time series from the financial domain, specifically stock prices and traded volume. We compute a large number of features extracted from postings (“tweets”) related to certain publicly-traded companies. Our goal is to find out which of these features are more correlated with changes in the stock of the companies. We start by carefully creating filters to select the relevant tweets for a company. We study various filtering approaches.
