Recurrent Graph Neural Networks for Rumor Detection in Online Forums
This work addresses the need for effective rumor detection in online forums, which is crucial for moderating misinformation, but it is incremental as it builds on existing graph neural network methods by adapting them to a specific data-scarce context.
The paper tackled the problem of classifying linked content like news articles or blogs in online forums such as Reddit, where traditional social graphs are unavailable, by using user interaction signals to derive a social graph and applying a Recurrent Graph Neural Network (R-GNN) encoder, achieving superior results in news link categorization and rumor detection compared to recent baselines.
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.