The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

中文翻译：

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信息级联分析综述

我们日常生活中大量的数字信息——从用户生成的内容，如微博和科学论文，到在线业务，如病毒式营销和广告——为探索和利用信息演变的轨迹和结构提供了前所未有的机会级联。学术界和工业界的大量研究工作旨在更好地了解推动信息传播的机制并量化信息传播的结果。本文对信息流行度预测方法进行了全面的回顾和分类，来自特征工程和随机过程， 通过图形表示， 到基于深度学习的方法. 具体来说，我们首先正式定义了不同类型的信息级联，并总结了现有研究的观点。然后，我们提出了一个分类法，将现有工作分为上述三个主要组以及每个组中的主要子类，并系统地回顾前沿研究工作。最后，我们总结了现有研究工作的优缺点，并概述了该领域的开放挑战和机遇。