The Dual Micro/Macro Informing Role of Social Network Sites: Can Twitter Macro Messages Help Predict Stock Prices?

摘要

This study proposes a framework for understanding the role of Social Network Sites (SNS) in informing clients at individual message (micro) and aggregate (macro) levels. To validate the micro/macro informer framework, we examine if an aggregate of Twitter messages can be used as a predictor of future stock prices of publicly traded companies. Our field study analyzes Twitter posts related to 18 Fortune500 companies using Latent Semantic Analysis to extract the semantic and conceptual content in the form of key themes. Using the factors comprising the themes, we fit a regression model that uses tweet volume and tweet topic strength to predict 8.3% of variability in stock prices beyond what is explained by the fluctuations of the stock market. Our results suggest that Twitter can be viewed as a macro informer for stock markets. This confirms our conjecture that SNS are both platforms for micro informing and macro informers. Our study should stimulate informing science researchers to examine other cases of macro informing and the role of data mining and text mining technologies in message aggregation for macro informing.