  When a piece of information (microblog, photograph, video, link, etc.) starts
to spread in a social network, an important question arises: will it spread to
viral proportions - where viral can be defined as an order-of-magnitude
increase. However, several previous studies have established that cascade size
and frequency are related through a power-law - which leads to a severe
imbalance in this classification problem. In this paper, we devise a suite of
measurements based on structural diversity - the variety of social contexts
(communities) in which individuals partaking in a given cascade engage. We
demonstrate these measures are able to distinguish viral from non-viral
cascades, despite the severe imbalance of the data for this problem. Further,
we leverage these measurements as features in a classification approach,
successfully predicting microblogs that grow from 50 to 500 reposts with
precision of 0.69 and recall of 0.52 for the viral class - despite this class
comprising under 2% of samples. This significantly outperforms our baseline
approach as well as the current state-of-the-art. We also show this approach
also performs well for identifying if cascades observed for 60 minutes will
grow to 500 reposts as well as demonstrate how we can tradeoff between
precision and recall.

Social and Information Networks;Physics and Society