Joint Modeling of Text and Networks for Cascade Prediction.

A critical research problem about information cascades, which is a central topic of social network analysis, is to predict the potential influence or the future growth of cascades. Recent developments of deep learning have provided promising alternatives, which no longer rely on heavy feature engineering efforts and instead learn the representation of cascade graphs in an end-to-end manner. In reality, however, the influence of a cascade not only depends on the cascade graph and the global network structure, but also largely relies on the content of the cascade and the preferences of users. In this work, we extend the deep learning approaches to cascade prediction by jointly modeling the content and the structure of cascades. We find that text information provides a valuable addition for the learning of cascade graphs, especially when some users (nodes) have rarely participated in the past cascades. To this end, a gating mechanism is introduced to dynamically fuse the structural and textual representations of nodes based on their respective properties. Attentions are employed to incorporate the text information associated with both cascade items and nodes. Empirical experiments demonstrate that incorporating text information brings a significant improvement to cascade prediction, and that the proposed model outperforms alternative ways to combine text and networks.