Emma Jingfei Zhang

Clustered Temporal Point Process Model with Application to Sina Weibo User Data

In this paper, we propose a new class of clustered temporal point process model to model the posting patterns of users of Sina Weibo, the largest twitter-type online social media in China. The proposed model captures both inhomogeneity in the initial posting time as well as the clustering pattern in the subsequent posts following the initial post. We develop two EM-type algorithms for estimating the parameters in the proposed model, based on which we cluster the different user patterns. In the application to real data, we discover interesting subgroups of users with distinct behaviors in terms of their initial posts and subsequent posts following the initial posts.