Summary of A Multimodal Adaptive Graph-based Intelligent Classification Model For Fake News, by Jun-hao (leo) Xu
A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News
by Jun-hao
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multimodal Adaptive Graph-based Intelligent Classification (MAGIC) detects fake news by leveraging geometric deep learning on graph-based structures. The approach combines transformer-based text vectorization and ResNet50 image processing with an adaptive graph attention network to build a comprehensive information interaction graph. This graph is then classified using the Softmax function, achieving state-of-the-art performance of 98.8% on Fakeddit (English) and 86.3% on Multimodal Fake News Detection (Chinese). Ablation experiments further demonstrate MAGIC’s superiority over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAGIC is a new way to spot fake news online. It uses special computer programs called transformers and ResNet50 to look at text and images together, like how you read the headlines of an article along with its images. This helps MAGIC build a map of what’s important in each piece of news. Then, it uses this map to decide if the news is real or fake. In tests, MAGIC did really well, correctly identifying almost 99% of English fake news and about 86% of Chinese fake news. It even beat other top methods for detecting fake news. |
Keywords
» Artificial intelligence » Classification » Deep learning » Graph attention network » Softmax » Transformer » Vectorization
