Elements of information theory
Elements of information theory
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Grouped graphical Granger modeling methods for temporal causal modeling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Journal of Computational Neuroscience
Information-theoretic measures of influence based on content dynamics
Proceedings of the sixth ACM international conference on Web search and data mining
Information cascade at group scale
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Are there cultural differences in event driven information propagation over social media?
Proceedings of the 2nd international workshop on Socially-aware multimedia
Proceedings of the 19th international conference on Intelligent User Interfaces
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Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information--theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine--grain notions of influence, and admits a natural, predictive interpretation. Networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.