# Recurrent Graph Neural Networks for Rumor Detection in Online Forums

arXiv: https://arxiv.org/abs/2108.03548 · HTML: https://mlsanity.com/paper/2108.03548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Fake News Detection via NLP is Vulnerable to Adversarial Attacks](https://mlsanity.com/paper/1901.09657/) (2019)
- [A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone](https://mlsanity.com/paper/2002.00850/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics](https://mlsanity.com/paper/2407.07038/) (2024)

## Abstract (arXiv)

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.