Peer and authority pressure in information-propagation models

Authors:
Aris Anagnostopoulos;George Brova;Evimaria Terzi
Affiliations:
Department of Computer and System Sciences, Sapienza University of Rome;Computer Science Department, Boston University;Computer Science Department, Boston University
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Year:
2011

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Abstract

Existing models of information diffusion assume that peer influence is the main reason for the observed propagation patterns. In this paper, we examine the role of authority pressure on the observed information cascades. We model this intuition by characterizing some nodes in the network as "authority" nodes. These are nodes that can influence large number of peers, while themselves cannot be influenced by peers. We propose a model that associates with every item two parameters that quantify the impact of the peer and the authority pressure on the item's propagation. Given a network and the observed diffusion patterns of the item, we learn these parameters from the data and characterize the item as peer- or authority-propagated. We also develop a randomization test that evaluates the statistical significance of our findings and makes our item characterization robust to noise. Our experiments with real data from online media and scientific-collaboration networks indicate that there is a strong signal of authority pressure in these networks.