  Information cascades exist in a wide variety of platforms on Internet. A very
important real-world problem is to identify which information cascades can go
viral. A system addressing this problem can be used in a variety of
applications including public health, marketing and counter-terrorism. As a
cascade can be considered as compound of the social network and the time
series. However, in related literature where methods for solving the cascade
prediction problem were proposed, the experimental settings were often limited
to only a single metric for a specific problem formulation. Moreover, little
attention was paid to the run time of those methods. In this paper, we first
formulate the cascade prediction problem as both classification and regression.
Then we compare three categories of cascade prediction methods: centrality
based, feature based and point process based. We carry out the comparison
through evaluation of the methods by both accuracy metrics and run time. The
results show that feature based methods can outperform others in terms of
prediction accuracy but suffer from heavy overhead especially for large
datasets. While point process based methods can also run into issue of long run
time when the model can not well adapt to the data. This paper seeks to address
issues in order to allow developers of systems for social network analysis to
select the most appropriate method for predicting viral information cascades.
