# MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

arXiv: https://arxiv.org/abs/2506.01627 · HTML: https://mlsanity.com/paper/2506.01627/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [CED: Credible Early Detection of Social Media Rumors](https://mlsanity.com/paper/1811.04175/) (2018)
- [Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection](https://mlsanity.com/paper/1704.05973/) (2017)
- [Fake News Identification on Twitter with Hybrid CNN and RNN Models](https://mlsanity.com/paper/1806.11316/) (2018)
- [Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection](https://mlsanity.com/paper/2107.10747/) (2021)
- [Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor](https://mlsanity.com/paper/2011.00259/) (2020)

## Built on by (most cited first, 3 of 3)

- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection](https://mlsanity.com/paper/2512.20670/) (2025)
- [LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection](https://mlsanity.com/paper/2603.19293/) (2026)

## Abstract (arXiv)

Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.