  Without having direct access to the information that is being exchanged,
traces of information flow can be obtained by looking at temporal sequences of
user interactions. These sequences can be represented as causality trees whose
statistics result from a complex interplay between the topology of the
underlying (social) network and the time correlations among the communications.
Here, we study causality trees in mobile-phone data, which can be represented
as a dynamical directed network. This representation of the data reveals the
existence of super-spreaders and super-receivers. We show that the tree
statistics, respectively the information spreading process, are extremely
sensitive to the in-out degree correlation exhibited by the users. We also
learn that a given information, e.g., a rumor, would require users to
retransmit it for more than 30 hours in order to cover a macroscopic fraction
of the system. Our analysis indicates that topological node-node correlations
of the underlying social network, while allowing the existence of information
loops, they also promote information spreading. Temporal correlations, and
therefore causality effects, are only visible as local phenomena and during
short time scales. These results are obtained through a combination of theory
and data analysis techniques.
