A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data

In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data. The proposed model can flexibly accommodate the incremental arriving of the social documents in an online manner by leveraging Dirichlet Process, and a density based technique is exploited to deduce the temporal dynamics of events. The spatial patterns of events are also incorporated in the model by a mixture of Gaussians. To remove the bias caused by the streaming process of the documents, Sequential Monte Carlo is used for the parameter inference. Our extensive experiments over two different real datasets show that the proposed model is capable to extract interpretable events effectively in terms of perplexity and coherence.