Information Cascade Prediction under Public Emergencies: A Survey

Autor:	Zhang, Qi, Wang, Guang, Lin, Li, Xia, Kaiwen, Wang, Shuai
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks Computer Science - Artificial Intelligence Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2007.09815 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.01319 Zobrazit plný text záznamu View this record from Arxiv