Liang Zhao, Feng Chen, Naren Ramakrishnan


Social media has been utilized as a significant surrogate for spatial societal event forecasting. The accuracy and discernibility of a spatial event forecasting model are two key concerns, as they determine how accurate and how detailed the model's predictions will be. Existing research focuses almost exclusively on the accuracy alone, seldom considering the accuracy and discernibility simultaneously because this would require a considerably more sophisticated model while suffering from several challenges, namely: 1) the precise formulation of the tradeoff between accuracy and discernibility; 2) the scarcity of social media data with a high spatial resolution; and 3) the characterization of spatial correlation and heterogeneity. This paper proposes a novel feature learning framework that concurrently addresses all the above challenges by formulating prediction tasks for different locations with different spatial resolutions, allowing the heterogeneous relationships among the tasks to be characterized. This characterization is then integrated into our new models based on multitask learning, with parameters optimized by our proposed algorithm based on the alternative direction method of multipliers (ADMM) and dynamic programming. Extensive experimental evaluations performed on several data sets from different domains demonstrated the effectiveness of our proposed approach.


Liang Zhao

Naren Ramakrishnan

Feng Chen

Publication Details

Date of publication:
August 16, 2017
Proceedings of the IEEE
Page number(s):
1953 - 1970
Issue Number:
Publication note:

Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Spatial Event Forecasting in Social Media With Geographically Hierarchical Regularization. Proc. IEEE 105(10): 1953-1970 (2017)