Discovering Cooperative Structure Among Online Items for Attention Dynamics

Social Media allows people to post widely and share the posted online-items. Such items gain their popularity by the amount of attention received. Thus, studies on modeling the arrival process of attention to an individual item have recently attracted a great deal of interest. In this paper, we propose, by combining a Dirichlet process with a Hawkes process in a novel way, a probabilistic model, called cooperative Hawkes process (CHP) model, to discover the cooperative structure among all the items involved. The proposed model takes into account all the arrival processes of shares for those items. We develop an efficient method of inferring the CHP model from the observed sequences of share events, and present an effective framework for predicting the future popularity of each of these items. Using synthetic data and real data from a cooking-recipe sharing site, we demonstrate the effectiveness of the CHP model, and uncover the cooperative structure among cooking-recipes in view of attention dynamics.