Online Protest Prediction with Time-Series Text and High-Order Interactive Topology

Aiming at the problem of neglecting user text timing differences and high-level interactive topologies among users in online protest prediction, combining temporal text and high-order interactive topology, an online protest prediction method was proposed. Modeling the influence of the text information published by users at different moments on their current protest tendency based on a self-attention mechanism, the user text representation vector was constructed. At the same time, the similarity of the neighbor nodes was used to construct the user interaction topology representation vector, maintaining the second-order similarity. Synthesizing the user text representation vector and the interactive representation vector, the user protest tendency was predicted. The results of the Twitter dataset show that the accuracy of the method can reach 93.9%, providing technical support for protest prediction.