Summary of A Self-learning Multimodal Approach For Fake News Detection, by Hao Chen et al.
A Self-Learning Multimodal Approach for Fake News Detection
by Hao Chen, Hui Guo, Baochen Hu, Shu Hu, Jinrong Hu, Siwei Lyu, Xi Wu, Xin Wang
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces a self-learning multimodal model for fake news classification that leverages contrastive learning and Large Language Models (LLMs) to analyze both text and image features. The model operates without requiring labeled data, making it suitable for large-scale applications. It outperforms several state-of-the-art classification approaches on a public dataset, achieving over 85% accuracy, precision, recall, and F1-score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to spot fake news online that uses machine learning. Right now, there’s a lot of false information being shared on social media, which can be bad for society. To help fix this problem, the researchers created a special computer model that looks at both words and pictures together. This helps the model understand what’s real and what’s not. The new approach works really well, beating other methods in tests. |
Keywords
» Artificial intelligence » Classification » F1 score » Machine learning » Precision » Recall
