document: Information transfer in social media 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 .
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