With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies. However, the involvement of specialist knowledge from various disciplines has resulted in a primarily application-specific focus (e.g., earthquakes, floods, infectious diseases) for information cascade prediction of public emergencies. The lack of a unified prediction framework poses a challenge for classifying intersectional prediction methods across different application fields. This survey paper offers a systematic classification and summary of information cascade modeling, prediction, and application. We aim to help researchers identify cutting-edge research and comprehend models and methods of information cascade prediction under public emergencies. By summarizing open issues and outlining future directions in this field, this paper has the potential to be a valuable resource for researchers conducting further studies on predicting information cascades.

翻译：随着大数据时代的到来，海量信息、专家经验和高精度模型为公共突发事件的信息级联预测带来了巨大机遇。然而，多学科专业知识的介入导致公共突发事件信息级联预测主要针对特定应用场景（例如地震、洪水、传染病等）。跨不同应用领域的交叉预测方法缺乏统一框架，构成了分类挑战。本综述对信息级联建模、预测及应用进行了系统分类与总结，旨在帮助研究者识别前沿研究，理解公共突发事件下信息级联预测的模型与方法。通过总结该领域的现存问题并展望未来发展方向，本文可为研究者深入探究信息级联预测提供重要参考。