# Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection

arXiv: https://arxiv.org/abs/2107.11934 · HTML: https://mlsanity.com/paper/2107.11934/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://mlsanity.com/paper/1902.02476/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Bayesian graph convolutional neural networks for semi-supervised classification](https://mlsanity.com/paper/1811.11103/) (2018)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Interpretable Rumor Detection in Microblogs by Attending to User Interactions](https://mlsanity.com/paper/2001.10667/) (2020)
- [Jointly embedding the local and global relations of heterogeneous graph for rumor detection](https://mlsanity.com/paper/1909.04465/) (2019)
- [Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection](https://mlsanity.com/paper/1704.05973/) (2017)
- [Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity](https://mlsanity.com/paper/1909.08211/) (2019)
- [Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling](https://mlsanity.com/paper/1911.04695/) (2019)

## Built on by (most cited first, 30 of 34)

- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Rumor Detection with Self-supervised Learning on Texts and Social Graph](https://mlsanity.com/paper/2204.08838/) (2022)
- [It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits](https://mlsanity.com/paper/2302.03147/) (2023)
- [Why Misinformation is Created? Detecting them by Integrating Intent Features](https://mlsanity.com/paper/2407.19196/) (2024)
- [Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection](https://mlsanity.com/paper/2508.07201/) (2025)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)
- [HQP: A Human-Annotated Dataset for Detecting Online Propaganda](https://mlsanity.com/paper/2304.14931/) (2023)
- [Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://mlsanity.com/paper/2212.10778/) (2022)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [VGA: Vision and Graph Fused Attention Network for Rumor Detection](https://mlsanity.com/paper/2401.01759/) (2024)
- [Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph](https://mlsanity.com/paper/2406.09884/) (2024)
- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
- [PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2203.04616/) (2022)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [Emulating Reader Behaviors for Fake News Detection](https://mlsanity.com/paper/2306.15231/) (2023)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Towards Data-centric Machine Learning on Directed Graphs: a Survey](https://mlsanity.com/paper/2412.01849/) (2024)
- [MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation](https://mlsanity.com/paper/2505.15834/) (2025)
- [Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model](https://mlsanity.com/paper/2508.07209/) (2025)
- [Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media](https://mlsanity.com/paper/2212.02419/) (2022)
- [Rumour detection using graph neural network and oversampling in benchmark Twitter dataset](https://mlsanity.com/paper/2212.10080/) (2022)
- [Transferring Structure Knowledge: A New Task to Fake news Detection Towards Cold-Start Propagation](https://mlsanity.com/paper/2407.09894/) (2024)
- [Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning](https://mlsanity.com/paper/2508.07205/) (2025)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [FaKnow: A Unified Library for Fake News Detection](https://mlsanity.com/paper/2401.16441/) (2024)
- [Advanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media](https://mlsanity.com/paper/2502.16157/) (2025)
- [Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models](https://mlsanity.com/paper/2503.08495/) (2025)
- [A Cross-Modal Rumor Detection Scheme via Contrastive Learning by Exploring Text and Image internal Correlations](https://mlsanity.com/paper/2508.11141/) (2025)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)

## Abstract (arXiv)

Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.