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.

中文翻译：

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利用时空动态图表示的多模式融合预测社会事件

大数据已在世界多个领域成功地用于解决社会问题。社会事件预测关系到社会稳定和可持续发展。然而，目前的研究很少考虑到事件参与者与学习社会事件的强大特征表示之间的动态联系。受图神经网络的启发，我们提出了一种新颖的孪生时空动态网络来预测社会事件。具体来说，我们使用包含新闻文章和全球事件的多模式数据来构建基于单词共现和事件参与者之间交互的动态图。动态图可以模拟社会事件的演变。通过融合来自异构历史数据的空间和时间动态图形表示，我们提出的模型预测目标国家未来社会事件的发生。对多个真实数据集的实验结果的定性和定量分析表明，我们提出的方法与几种社交事件预测方法相比具有竞争力。