Measuring user influence in financial microblogs: experiments using stocktwits data

Abstract

In this paper, we study the effect of graph structure user in- fluence measures in financial social media. In particular, we explore rich and recent data, composed of 1.2 million Stock- Twits messages, from June 2010 to March 2013. These data allow the creation of social network graphs by considering direct active interactions (retweets, shares or replies). Using such graphs and a realistic rolling windows evaluation, we analyzed four user influence measures (indegree, between- ness, page rank and posts) under two criteria: Percentage of Quality Users (PQU), as manually labeled by StockTwits; and the daily sentiment correlation between top lists of in- fluential users and other users. The sentiment was based on a StockTwits labeled dataset and assessed in terms of three selections: overall sentiment (ALL) and filtered by two ma- jor technological companies (Apple – AAPL and Google – GOOG). Promising results were obtained, with several top lists pre- senting PQU values higher than 80% and correlations higher than 0.6. Overall, the best results were achieved by the page rank and posts measures.