Predicting Social Events with Multimodal Fusion of Spatial and Temporal Dynamic Graph Representations

Big data has been satisfactorily used to solve social issues in several parts of the word. Social event prediction is related to social stability and sustainable development. However, current research rarely takes into account the dynamic connections between event actors and learning robust feature representations of social events. Inspired by the graph neural network, we propose a novel Siamese Spatial and Temporal Dynamic Network for predicting social events. Specifically, we use multimodal data containing news articles and global events to construct dynamic graphs based on word co-occurrences and interactions between event actors. Dynamic graphs can model the evolution of social events. By employing the fusion of spatial and temporal dynamic graph representations from heterogeneous historical data, our proposed model predicts the occurrence of future social events for the target country. Qualitative and quantitative analysis of experiment results on multiple real-word datasets shows that our proposed method is competitive against several approaches for social event prediction.