A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

Abstract

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.

arXiv commentAuthor version, with 43 pages, 9 figures, and 11 tables

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Cited by in corpus (13)

• Universality, criticality and complexity of information propagation in social media
• CCGL: Contrastive Cascade Graph Learning
• Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction
• Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques
• CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks
• Graph Representation Learning for Popularity Prediction Problem: A Survey
• Hyperdimensional Representation Learning for Node Classification and Link Prediction
• DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property
• Modeling diffusion in networks with communities: a multitype branching process approach
• Information Diffusion Prediction with Latent Factor Disentanglement
• Popularity Prediction for Social Media over Arbitrary Time Horizons
• Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement
• One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories