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

TL;DR

This paper introduces MAGIC, a graph-based deep learning model that effectively detects multimodal fake news by integrating text and image features through adaptive graph attention networks, achieving high accuracy on multiple datasets.

Contribution

The paper presents a novel graph-based adaptive model for multimodal fake news detection, combining transformer and ResNet features with graph attention networks.

Findings

Achieved 98.8% accuracy on Fakeddit dataset.

Achieved 86.3% accuracy on Chinese fake news dataset.

Outperformed existing state-of-the-art methods in fake news detection.

Abstract

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.