Multi-scale characterization of social network dynamics in the blogosphere

A computational framework to characterize social network dynamics in the blogosphere at individual, group and community levels is developed and an innovative method to establish the utility of the extracted knowledge is proposed, by correlating the mined knowledge with an external time series data (the stock market).

Abstract

We have developed a computational framework to characterize social network dynamics in the blogosphere at individual, group and community levels. Such characterization could be used by corporations to help drive targeted advertising and to track the moods and sentiments of consumers. We tested our model on a widely read technology blog called Engadget. Our results show that communities transit between states of high and low entropy, depending on sentiments (positive / negative) about external happenings. We also propose an innovative method to establish the utility of the extracted knowledge, by correlating the mined knowledge with an external time series data (the stock market). Our validation results show that the characterized groups exhibit high stock market movement predictability (89%) and removal of ‘impactful ’ groups makes the community less resilient by lowering predictability (26%) and affecting the composition of the groups in the rest of the community.