  The widespread adoption of online social networks in daily life has created a
pressing need for effectively classifying user-generated content. This work
presents techniques for classifying linked content spread on forum websites --
specifically, links to news articles or blogs -- using user interaction signals
alone. Importantly, online forums such as Reddit do not have a user-generated
social graph, which is assumed in social network behavioral-based
classification settings. Using Reddit as a case-study, we show how to obtain a
derived social graph, and use this graph, Reddit post sequences, and comment
trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder. We train
the R-GNN on news link categorization and rumor detection, showing superior
results to recent baselines. Our code is made publicly available at
https://github.com/google-research/social_cascades.
