Human Dynamics

Information Spreading in Context

Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.


More publications
D. Wang, D. Pedreschi, C. Song, F. Giannotti, A.-L. Barabasi

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , (2011)

J. P. Onnela, S. Arbesman, M. C. Gonzalez, A.-L. Barabasi, N. A. Christakis

PLoS One 6:4, 1-7 (2011)

Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási

Nature 473, 167-173 (2011)