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Learning the Nature of Information in Social Networks

Rakesh Agrawal, Michalis Potamias, and Evimaria Terzi

Abstract

We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openness by introducing the receptivity of a node. Given a social network and data related to the ordering of adoption of information items by nodes, we develop a maximum-likelihood framework for estimating endogeneity, exogeneity and receptivity parameters. We apply our methodology to synthetic and real data and demonstrate its efficacy as a data-analytic tool.

Details

Publication typeInproceedings
Published in6th Iternational AAAI Conference on Weblogs and Social Media (ICWSM)
PublisherAmerican Association for Artificial Intelligence
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