# Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks

arXiv: https://arxiv.org/abs/2110.04522 · HTML: https://mlsanity.com/paper/2110.04522/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (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)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [Interpretable Rumor Detection in Microblogs by Attending to User Interactions](https://mlsanity.com/paper/2001.10667/) (2020)
- [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://mlsanity.com/paper/1512.08422/) (2015)
- [Jointly embedding the local and global relations of heterogeneous graph for rumor detection](https://mlsanity.com/paper/1909.04465/) (2019)
- [Simple and Effective Text Matching with Richer Alignment Features](https://mlsanity.com/paper/1908.00300/) (2019)
- [Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media](https://mlsanity.com/paper/1610.07363/) (2016)
- [Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension](https://mlsanity.com/paper/2005.05806/) (2020)
- [Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter](https://mlsanity.com/paper/2006.05866/) (2020)

## Built on by (most cited first, 29 of 29)

- [GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse](https://mlsanity.com/paper/2401.01523/) (2024)
- [Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning](https://mlsanity.com/paper/2204.08143/) (2022)
- [Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning](https://mlsanity.com/paper/2212.01117/) (2022)
- [Towards Low-Resource Harmful Meme Detection with LMM Agents](https://mlsanity.com/paper/2411.05383/) (2024)
- [FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models](https://mlsanity.com/paper/2502.17924/) (2025)
- [Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models](https://mlsanity.com/paper/2401.13298/) (2024)
- [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)
- [MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models](https://mlsanity.com/paper/2406.11288/) (2024)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning](https://mlsanity.com/paper/2203.11720/) (2022)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models](https://mlsanity.com/paper/2405.00390/) (2024)
- [WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom](https://mlsanity.com/paper/2310.16579/) (2023)
- [LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information](https://mlsanity.com/paper/2502.08888/) (2025)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024)
- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
- [Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking](https://mlsanity.com/paper/2601.02669/) (2026)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets](https://mlsanity.com/paper/2309.11576/) (2023)
- [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)
- [A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection](https://mlsanity.com/paper/2304.01492/) (2023)
- [Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection](https://mlsanity.com/paper/2404.16076/) (2024)
- [Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms](https://mlsanity.com/paper/2512.15793/) (2025)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)
- [MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models](https://mlsanity.com/paper/2510.27196/) (2025)

## Abstract (arXiv)

Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.