Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan


Event forecasting in Twitter is an important and challenging problem. Most existing approaches focus on forecasting temporal events (such as elections and sports) and do not consider spatial features and their underlying correlations. In this paper, we propose a generative model for spatiotemporal event forecasting in Twitter. Our model characterizes the underlying development of future events by jointly modeling the structural contexts and spatiotemporal burstiness. An effective inference algorithm is developed to train the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental evaluations on two different domains demonstrated the effectiveness of our proposed approach.


Liang Zhao

Naren Ramakrishnan

Feng Chen

Chang-Tien Lu

Publication Details

Date of publication:
April 30, 2015
SIAM International Conference