# Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter

arXiv: https://arxiv.org/abs/2006.05866 · HTML: https://mlsanity.com/paper/2006.05866/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention](https://mlsanity.com/paper/2303.11945/) (2023)
- [Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs](https://mlsanity.com/paper/2207.08098/) (2022)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks](https://mlsanity.com/paper/2212.10778/) (2022)
- [Heterogeneous Social Event Detection via Hyperbolic Graph Representations](https://mlsanity.com/paper/2302.10362/) (2023)
- [AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach](https://mlsanity.com/paper/2208.08200/) (2022)
- [Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning](https://mlsanity.com/paper/2508.07205/) (2025)
- [A Meta Path-based Approach for Rumor Detection on Social Media](https://mlsanity.com/paper/2301.04341/) (2023)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)

## Abstract (arXiv)

With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The openness and convenience of social media platforms provide a free expression for people but also cause new social problems. The widespread of false rumors on social media can bring about the panic of the public and damage personal reputation, which makes rumor automatic detection technology become particularly necessary. The majority of existing methods for rumor detection focus on mining effective features from text contents, user profiles, and patterns of propagation. Nevertheless, these methods do not take full advantage of global semantic relations of the text contents, which characterize the semantic commonality of rumors as a key factor for detecting rumors. In this paper, we construct a tweet-word-user heterogeneous graph based on the text contents and the source tweet propagations of rumors. A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection. Experiments on real-world Twitter data demonstrate the superiority of the proposed approach, which also has a comparable ability to detect rumors at a very early stage.