  As a means of modern communication tools, online discussion forums have
become an increasingly popular platform that allows asynchronous online
interactions. People share thoughts and opinions through posting threads and
replies, which form a unique communication structure between main threads and
associated replies. It is significant to understand the information diffusion
pattern under such a communication structure, where an essential task is to
predict the arrival time of future events. In this work, we proposed a novel
yet simple framework, called SocialGrid, for modeling events in online
discussing forms. Our framework first transforms the entire event space into a
grid representation by grouping successive evens in one time interval of a
particular length. Based on the nature of the grid, we leverage the Temporal
Convolution Network to learn the dynamics at the grid level. Varying the
temporal scope of an individual grid, the learned grid model can be used to
predict the arrival time of future events at different granularities.
Leveraging the Reddit data, we validate the proposed method through experiments
on a series of applications. Extensive experiments and a real-world
application. Results have shown that our framework excels at various cascade
prediction tasks comparing with other approaches.
