Information Cascade Prediction under Public Emergencies: A Survey
Qi Zhang, Guang Wang, Li Lin, Kaiwen Xia, Shuai Wang
TL;DR
This paper surveys information cascade prediction under public emergencies, addressing the absence of a unified framework across disaster types. It presents a taxonomy of methods organized by temporal, structural, user/item, and content features, and analyzes how time, location, semantics, and tensor-based representations enable joint deductions about future emergencies. The review covers prediction tasks, modeling approaches (including cascade/global/r-reachable graphs and tensor methods), and applications from early warning to disaster response, while discussing challenges in complexity, interpretability, robustness, and human-AI collaboration. The work provides a comprehensive, up-to-date synthesis with a roadmap for future research and practical guidance for researchers and practitioners. The practical impact lies in offering a structured understanding to improve timely and accurate emergency predictions and decision-making across multiple domains.
Abstract
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.