用图注意力网络融合图文信息，精准识别真假新闻
A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News
- 构建自适应图结构，融合文本与图像特征
- 在英文和中文数据集上准确率分别达98.8%和86.3%
- 适合多模态虚假信息检测研究者参考
现有虚假新闻检测研究多聚焦于多模态机器学习方法，但基于几何深度学习的图结构研究仍不足。为此，本文提出多模态自适应图智能分类模型（MAGIC），利用BERT进行文本向量化，ResNet50处理图像，通过自适应图注意力网络构建综合信息交互图，并以Softmax函数完成分类。模型在英文数据集Fakeddit和中文数据集Multimodal Fake News Detection上分别达到98.8%和86.3%的准确率。消融实验表明，MAGIC在两个数据集上均表现优于现有方法，验证了图结构深度学习在多模态虚假新闻检测中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。