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Social media allows people to post widely and evaluate diverse information including ideas, news and opinions. Once such an online item is posted on a social media site, it can be appreciated and shared by many people and become popular. This kind of phenomenon can have a large influence on people’s daily life and social trends. Thus, studies on modeling the arrival process of shares to an individual item have recently attracted a great deal of interest in the field of social media mining. 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 and real data, we demonstrate that the CHP model outperforms the Hawkes process model without interaction among items (HP model) and the multivariate Hawkes process model (MHP model) in terms of popularity prediction. Moreover, for real data from a cooking-recipe sharing site, we discover the cooperative structure among cooking-recipes in view of popularity dynamics by applying the CHP model.

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