document: PET : a statistical model for popular events tracking in social communities User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time . A good example is a web-blogging community with the daily blog posts and a social network of bloggers . An important task of analyzing an online community is to observe and track the popular events , or topics that evolve over time in the community . Existing approaches usually focus on either the burstiness of topics or the evolution of networks , but ignoring the interplay between textual topics and network structures . In this paper , we formally define the problem of popular event tracking in online communities ( PET ) , focusing on the interplay between texts and networks . We propose a novel statistical method that models the the popularity of events over time , taking into consideration the burstiness of user interest , information diffusion on the network structure , and the evolution of textual topics . Specifically , a Gibbs Random Field is defined to model the influence of historic status and the dependency relationships in the graph ; thereafter a topic model generates the words in text content of the event , regularized by the Gibbs Random Field . We prove that two classic models in information diffusion and text burstiness are special cases of our model under certain situations . Empirical experiments with two different communities and datasets ( i.e. , Twitter and DBLP ) show that our approach is effective and outperforms existing approaches .
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