Blog Community Discovery and Evolution Based on Mutual Awareness Expansion
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Scalable discovery of contradictions on the web
Proceedings of the 19th international conference on World wide web
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
Extracting events and event descriptions from Twitter
Proceedings of the 20th international conference companion on World wide web
A time-varying propagation model of hot topic on BBS sites and Blog networks
Information Sciences: an International Journal
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
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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.