Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

By Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang. AAAI Conference on Artificial Intelligence, 2020

816 citations recorded by the source corpus.

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation; and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a3bc7baa3f249185a5d69e7830c7cd61290c15cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Xinyi Zhou
2. Tingyang Xu
3. Junzhou Huang
4. Yu Rong

In Statistical and Nonlinear Physics

1. Junzhou Huang
2. Yu Rong
3. Xinyi Zhou
4. Ying Wei

Citations in the corpus

• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020, background): “Inspired by the remarkable achievements of deep learning in many scientific domains, such as computer vision [19, 57], natural language processing [51, 53], and social networks [3, 31], researchers are exploiting deep learning approaches to accelerate the process of drug discovery and reduce costs b”
• A Survey of Fake News (2020, background): “For example, Ma et al. develop recursive neural networks (RvNNs), a tree-structured neural network, based on news cascades [Bian et al. 2020; Ma et al. 2018].”
• Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, background): “To tackle this problem, fake news detection has received increasing research attention in recent years [3, 4, 12, 20, 21, 33, 34].”