  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.
