The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Open accessAbstract
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.
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Cites
Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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node2vec: Scalable Feature Learning for Networks
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The spread of true and false news online
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“A few examples include: investigating which topology of the diffusion network expands faster [38], the temporal and textual characteristics of burst public events and fake news and why they are traveled fast [148, 193], and the visual explanations of why some images/videos become popular quickly and why others do not…”
BackgroundGraph neural networks: A review of methods and applications
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Cited by
Integrating explanation and prediction in computational social science
Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03659-0 · Cited by 371
Metadata
- Authors
- Zhou, F., Xu, X., Trajcevski, G., & Zhang, K.
- Venue
- ACM Computing Surveys
- Published
- 2021-03-05
- Volume
- 54
- Issue
- 2
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3028571922
- Cited by
- 259
- References
- 21
- FWCI
- 16.59
- Type
- journal article
- Language
- en
- Open access
- bronze
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