Spatial event forecasting from social media is potentially extremely useful but suffers from critical challenges, such as the dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches (e.g., LASSO regression, dynamic query expansion, and burst detection) address some, but not all, of these challenges. Here, we propose a novel multi-task learning framework that aims to concurrently address all the challenges involved. Specifically, given a collection of locations (e.g., cities), forecasting models are built for all the locations simultaneously by extracting and utilizing appropriate shared information that effectively increases the sample size for each location, thus improving the forecasting performance. The new model combines both static features derived from a predefined vocabulary by domain experts and dynamic features generated from dynamic query expansion in a multi-task feature learning framework. Different strategies to balance homogeneity and diversity between static and dynamic terms are also investigated. And, efficient algorithms based on Iterative Group Hard Thresholding are developed to achieve efficient and effective model training and prediction. Extensive experimental evaluations on Twitter data from civil unrest and influenza outbreak datasets demonstrate the effectiveness and efficiency of our proposed approach.
['Liang Zhao', 'Qian Sun', 'Jieping Ye', 'Feng Chen', 'Chang-Tien Lu', 'Naren Ramakrishnan']
Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting