  The study of virality and information diffusion online is a topic gaining
traction rapidly in the computational social sciences. Computer vision and
social network analysis research have also focused on understanding the impact
of content and information diffusion in making content viral, with prior
approaches not performing significantly well as other traditional
classification tasks. In this paper, we present a novel pairwise reformulation
of the virality prediction problem as an attribute prediction task and develop
a novel algorithm to model image virality on online media using a pairwise
neural network. Our model provides significant insights into the features that
are responsible for promoting virality and surpasses the existing
state-of-the-art by a 12% average improvement in prediction. We also
investigate the effect of external category supervision on relative attribute
prediction and observe an increase in prediction accuracy for the same across
several attribute learning datasets.
