# Rumor Detection with a novel graph neural network approach

arXiv: https://arxiv.org/abs/2403.16206 · HTML: https://mlsanity.com/paper/2403.16206/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [SwitchTab: Switched Autoencoders Are Effective Tabular Learners](https://mlsanity.com/paper/2401.02013/) (2024)
- [CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting](https://mlsanity.com/paper/2310.16069/) (2023)
- [E(2)-Equivariant Vision Transformer](https://mlsanity.com/paper/2306.06722/) (2023)
- [ReConTab: Regularized Contrastive Representation Learning for Tabular Data](https://mlsanity.com/paper/2310.18541/) (2023)
- [Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04148/) (2023)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation](https://mlsanity.com/paper/2308.09917/) (2023)
- [News Recommendation with Attention Mechanism](https://mlsanity.com/paper/2402.07422/) (2024)
- [Neural Process for Black-Box Model Optimization Under Bayesian Framework](https://mlsanity.com/paper/2104.02487/) (2021)
- [Segment Any Building](https://mlsanity.com/paper/2310.01164/) (2023)
- [LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection](https://mlsanity.com/paper/2401.16001/) (2024)
- [Contextual Biasing of Named-Entities with Large Language Models](https://mlsanity.com/paper/2309.00723/) (2023)
- [Contrastive Knowledge Amalgamation for Unsupervised Image Classification](https://mlsanity.com/paper/2307.14781/) (2023)
- [Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data](https://mlsanity.com/paper/2111.14929/) (2021)
- [Sub2Full: split spectrum to boost OCT despeckling without clean data](https://mlsanity.com/paper/2401.10128/) (2024)
- [CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver](https://mlsanity.com/paper/2403.03391/) (2024)
- [Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2307.03638/) (2023)

## Built on by (most cited first, 15 of 15)

- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [Automatic News Generation and Fact-Checking System Based on Language Processing](https://mlsanity.com/paper/2405.10492/) (2024)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes](https://mlsanity.com/paper/2404.06050/) (2024)
- [Utilizing Deep Learning to Optimize Software Development Processes](https://mlsanity.com/paper/2404.13630/) (2024)
- [Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias](https://mlsanity.com/paper/2405.05506/) (2024)
- [Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2405.16464/) (2024)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields](https://mlsanity.com/paper/2406.18311/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Deep Reinforcement Learning for Mobile Robot Path Planning](https://mlsanity.com/paper/2404.06974/) (2024)
- [GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better](https://mlsanity.com/paper/2501.14427/) (2025)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability](https://mlsanity.com/paper/2407.00668/) (2024)
- [Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts](https://mlsanity.com/paper/2602.21214/) (2026)

## Abstract (arXiv)

The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors, while very few works focus on correlation among users that they may coordinate to spread rumors in order to gain large popularity. In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media. Specifically, we leverage graph neural networks to learn the representations of user correlation from a bipartite graph that describes the correlations between users and source tweets, and the representations of information propagation with a tree structure. Then we combine the learned representations from these two modules to classify the rumors. Since malicious users intend to subvert our model after deployment, we further develop a greedy attack scheme to analyze the cost of three adversarial attacks: graph attack, comment attack, and joint attack. Evaluation results on two public datasets illustrate that the proposed MODEL outperforms the state-of-the-art rumor detection models. We also demonstrate our method performs well for early rumor detection. Moreover, the proposed detection method is more robust to adversarial attacks compared to the best existing method. Importantly, we show that it requires a high cost for attackers to subvert user correlation pattern, demonstrating the importance of considering user correlation for rumor detection.