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


Event forecasting from social media data streams has many applications. Existing approaches focus on forecasting temporal events (such as elections and sports) but as yet cannot forecast spatiotemporal events such as civil unrest and influenza outbreaks, which are much more challenging. To achieve spatiotemporal event forecasting, spatial features that evolve with time and their underlying correlations need to be considered and characterized. In this article, we propose novel batch and online approaches for spatiotemporal event forecasting in social media such as Twitter. Our models characterize the underlying development of future events by simultaneously modeling the structural contexts and their spatiotemporal burstiness based on different strategies. Both batch and online-based inference algorithms are developed to optimize 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 demonstrate the effectiveness of our proposed approach.


Liang Zhao

Naren Ramakrishnan

Chang-Tien Lu

Feng Chen

Publication Details

Date of publication:
November 2, 2016
ACM Transactions on Spatial Algorithms and Systems (TSAS)
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