Multi-task learning for spatial events prediction from social data

Multi-task learning is becoming more popular and is being applied in a variety of applications. It improves the accuracy of prediction by simultaneously learning related tasks and saves cost through shared structures. In particular, the prediction of event type from social data is also an area where multi-task learning can be utilized. In this paper, we present a novel deep learning framework called Spatial Events Prediction (SEP) based on multi-task learning to predict the types of events that happen at a specific location from social data. The proposed model focuses on predicting the attribute types of an event, which is referred to as subtypes. Specifically, an event type-specific attention mechanism is introduced to extract the representations of social data and to identify their important components. The proposed attention mechanism is based on a two-level attention, which measures the importance of words and sentences to the subtypes of an event. We also propose a representation sharing method using semantic and spatial relationships between locations to alleviate the sparsity and incompleteness of data. The proposed representation sharing preserves the spatial heterogeneity between locations and significantly improves the accuracy of the overall framework. Experiments with real-world datasets confirm the effectiveness and efficiency of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.