Abstract
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. Consequently, the very idea that there is (intentional) information spreading beyond a small vecinity is called into question. These results are obtained through a combination of theory and data analysis techniques.
Highlights
Phone call activity patterns are a manifestation of our complex social dynamics
We have shown that the mobile phone data can be represented by a directed network, and argued that intentional information spreading requires information to flow in the direction given by the directed edges
We have shown that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation of the users
Summary
Phone call activity patterns are a manifestation of our complex social dynamics. Several aspects of our social behavior are reflected in these communication patterns, like day-night cycles, high activity at the end of working hours, or even our mobility patterns [1,2,3,4,5,6,7,8,9]. Mobile phone data provides an excellent ground to study several interesting social processes such as, for instance, the spreading of news and rumors, which is the focus of this work. Mobile phone log data consists in who calls whom and when, see Fig. 1A. A natural way of representing this data is through the use of directed edges. We have to associate to each directed edge a time series that symbolizes when (and how many times) this action took place. This procedure provides us with a representation of log data in terms of a directed network
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