Abstract
In a wide range of social unrest events prediction, the dynamic graph convolutional network (DGCN) has been successfully leveraged to achieve reliable performance. The innovation of DGCN mainly focuses on capturing the temporal features of unrest events. Inspired by the DGCN, we propose a new graph convolutional network model called Contextual Gated Graph Convolutional Network (CGGCN), which is adopted to predict and analyze social unrest events. The CGGCN uses the contextual gated layer, which improves the layer-wise propagation rules of graph convolutional networks. The contextual gated layer can re-learn the keyword representation to capture the contextual semantic features of unrest events by using squeeze and excitation module. The principle of the squeeze and excitation module is to increase the weight of meaningful words for event prediction and to suppress weaker ones. In our work, we obtain historical texts including published news and short tweets related to social unrest events. Based on these historical texts data, the CGGCN can predict the occurrence of social unrest events efficiently. In addition, we propose a method for establishing the evolution graph of unrest events. In this way, we can use several core words to summarize the evolution of the event. Finally, we conduct extensive experiments on the unrest events data sets. The experimental results show that the CGGCN leads by about 5%–7% in the performance of prediction compared with other mainstream methods.
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Wang, H., Zhou, B., Gu, Z., Jia, Y. (2021). Social Unrest Events Prediction by Contextual Gated Graph Convolutional Networks. In: Jia, Y., Gu, Z., Li, A. (eds) MDATA: A New Knowledge Representation Model. Lecture Notes in Computer Science(), vol 12647. Springer, Cham. https://doi.org/10.1007/978-3-030-71590-8_13
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