Jointly Predicting Civil Unrest Event Occurrence Probabilities and Potential Initiators via Multi-Task Learning

The eruption of civil unrest events often inflicts severe disruption on social stability. Accurate prediction of these events can assist decision-makers in proactively formulating countermeasures, thereby preventing violent incidents and mitigating property damage. Current event prediction methods often focus on predicting a single event element, such as occurrence probability or potential initiators, resulting in less reference information provided to decision-makers and an inability to formulate more specific response measures. To address this limitation, we formulate civil unrest forecasting as a multi-task learning problem and propose a parallel learning framework that jointly predicts social unrest event occurrence and potential initiators. In this framework, an event graph is first encoded by a graph neural module that combines graph attention mechanisms with graph convolutional networks, and the learned event representations are subsequently fed into task-specific predictors for joint forecasting. Extensive comparative experiments conducted across five datasets demonstrate that the proposed framework achieves superior predictive performance and high training efficiency. These findings can support earlier risk identification, improve the allocation of public-safety resources, and provide more informative evidence for mitigating the social disruption caused by civil unrest events.