# A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News

arXiv: https://arxiv.org/abs/2411.06097 · HTML: https://mlsanity.com/paper/2411.06097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues](https://mlsanity.com/paper/2108.10509/) (2021)
- [Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage](https://mlsanity.com/paper/2212.14727/) (2022)
- [Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media](https://mlsanity.com/paper/2212.02419/) (2022)

## Built on by (most cited first, 2 of 2)

- [UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation](https://mlsanity.com/paper/2502.11132/) (2025)
- [F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text](https://mlsanity.com/paper/2605.17115/) (2026)

## Abstract (arXiv)

Numerous studies have been proposed to detect fake news focusing on multi-modalities based on machine and/or deep learning. However, studies focusing on graph-based structures using geometric deep learning are lacking. To address this challenge, we introduce the Multimodal Adaptive Graph-based Intelligent Classification (aptly referred to as MAGIC) for fake news detection. Specifically, the Encoder Representations from Transformers was used for text vectorization whilst ResNet50 was used for images. A comprehensive information interaction graph was built using the adaptive Graph Attention Network before classifying the multimodal input through the Softmax function. MAGIC was trained and tested on two fake news datasets, that is, Fakeddit (English) and Multimodal Fake News Detection (Chinese), with the model achieving an accuracy of 98.8% and 86.3%, respectively. Ablation experiments also revealed MAGIC to yield superior performance across both the datasets. Findings show that a graph-based deep learning adaptive model is effective in detecting multimodal fake news, surpassing state-of-the-art methods.