# Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

arXiv: https://arxiv.org/abs/1704.05973 · HTML: https://mlsanity.com/paper/1704.05973/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 25 of 25)

- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [Rumor Detection with Self-supervised Learning on Texts and Social Graph](https://mlsanity.com/paper/2204.08838/) (2022)
- [Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection](https://mlsanity.com/paper/2306.02137/) (2023)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [VGA: Vision and Graph Fused Attention Network for Rumor Detection](https://mlsanity.com/paper/2401.01759/) (2024)
- [TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer](https://mlsanity.com/paper/2403.12481/) (2024)
- [MVAN: Multi-View Attention Networks for Fake News Detection on Social Media](https://mlsanity.com/paper/2506.01627/) (2025)
- [Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic](https://mlsanity.com/paper/2109.01850/) (2021)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling](https://mlsanity.com/paper/2312.03523/) (2023)
- [Crowd Intelligence for Early Misinformation Prediction on Social Media](https://mlsanity.com/paper/2408.04463/) (2024)
- [CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning](https://mlsanity.com/paper/2501.12422/) (2025)
- [TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities](https://mlsanity.com/paper/2509.19352/) (2025)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)
- [HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media](https://mlsanity.com/paper/2110.00425/) (2021)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Online Auditing of Information Flow](https://mlsanity.com/paper/2310.14595/) (2023)
- [Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection](https://mlsanity.com/paper/2501.14455/) (2025)
- [Multimodal rumor detection enhanced by external evidence and forgery features](https://mlsanity.com/paper/2601.14954/) (2026)

## Abstract (arXiv)

The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as textitearly rumor detection, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model on the basis of recurrent neural networks (RNN) to learn textitselectively temporal hidden representations of sequential posts for identifying rumors. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features; (2) the introduction of soft attention mechanism can effectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.