mlsanityData to 7 Oct 2026

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

arXiv 2110.04522 ↗First posted 9 Oct 2021cs.CLcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

18 of 44 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
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  8. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  9. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 108
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  11. 2015Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threadscited by 62
  12. 2020Interpretable Rumor Detection in Microblogs by Attending to User Interactionscited by 44
6 more
  1. 2015Natural Language Inference by Tree-Based Convolution and Heuristic Matchingcited by 42
  2. 2019Jointly embedding the local and global relations of heterogeneous graph for rumor detectioncited by 31
  3. 2019Simple and Effective Text Matching with Richer Alignment Featurescited by 29
  4. 2016Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Mediacited by 25
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  6. 2020Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twittercited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 5; 2024: 9; 2025: 6; 2026: 1.

29 papers.

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  1. 2024GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abusecited by 50
  2. 2022Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learningcited by 41
  3. 2022Zero-Shot Rumor Detection with Propagation Structure via Prompt Learningcited by 32
  4. 2024Towards Low-Resource Harmful Meme Detection with LMM Agentscited by 23
  5. 2025FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Modelscited by 19
  6. 2024Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Modelscited by 17
  7. 2022Rumor Detection with Self-supervised Learning on Texts and Social Graphcited by 15
  8. 2023It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splitscited by 15
  9. 2024MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Modelscited by 15
  10. 2025Large Language Models in Argument Mining: A Surveycited by 14
  11. 2022Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuningcited by 12
  12. 2022A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learningcited by 10
17 more
  1. 2024CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Modelscited by 8
  2. 2023WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdomcited by 6
  3. 2025LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Informationcited by 6
  4. 2024Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Modelscited by 5
  5. 2024TempoFormer: A Transformer for Temporally-aware Representations in Change Detectioncited by 5
  6. 2024Rumor Detection on Social Media with Temporal Propagation Structure Optimizationcited by 3
  7. 2026Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checkingcited by 3
  8. 2023Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Treescited by 2
  9. 2023Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasetscited by 2
  10. 2022Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Mediacited by 1
  11. 2022Rumour detection using graph neural network and oversampling in benchmark Twitter datasetcited by 1
  12. 2023A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detectioncited by 1
  13. 2024Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detectioncited by 1
  14. 2025Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Normscited by 1
  15. 2022Region-enhanced Deep Graph Convolutional Networks for Rumor Detectioncited by 0
  16. 2025Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Electioncited by 0
  17. 2025MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Modelscited by 0