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.
@article{arxiv.2108.03548,
title = {Recurrent Graph Neural Networks for Rumor Detection in Online Forums},
author = {Di Huang and Jacob Bartel and John Palowitch},
journal= {arXiv preprint arXiv:2108.03548},
year = {2021}
}