Measuring User Influence in Financial Microblogs: Experiments Using StockTwits Data

In this paper, we study the effect of graph structure user influence measures in financial social media. In particular, we explore rich and recent data, composed of 1.2 million StockTwits 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, betweenness, 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 influential 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 major technological companies (Apple -- AAPL and Google -- GOOG). Promising results were obtained, with several top lists presenting PQU values higher than 80% and correlations higher than 0.6. Overall, the best results were achieved by the page rank and posts measures.

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