Summary of Dynamic Analysis and Adaptive Discriminator For Fake News Detection, by Xinqi Su et al.
Dynamic Analysis and Adaptive Discriminator for Fake News Detection
by Xinqi Su, Zitong Yu, Yawen Cui, Ajian Liu, Xun Lin, Yuhao Wang, Haochen Liang, Wenhui Li, Li Shen, Xiaochun Cao
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection on online social networks. The current knowledge-based and semantic-based methods are limited by their reliance on human expertise and feedback, lacking flexibility. To address this challenge, the authors introduce a Monte Carlo Tree Search algorithm to leverage large language models (LLMs) for prompt optimization, providing richer domain-specific details and guidance to LLMs. Additionally, the paper defines four deceit patterns: emotional exaggeration, logical inconsistency, image manipulation, and semantic inconsistency, to reveal the mechanisms behind fake news creation. Four discriminators are designed to detect these patterns using a soft-routing mechanism to explore optimal detection models. Experimental results on three real-world datasets demonstrate the superiority of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is spreading quickly online and causing problems for society. Current methods for detecting fake news rely too much on human help, which isn’t flexible enough. This paper proposes a new way to detect fake news using artificial intelligence. It uses big language models to analyze news articles and identify patterns that are common in fake news. The authors also define four ways that fake news is often created: exaggerating emotions, being illogical, manipulating images, and being semantically inconsistent. They then design special tools called discriminators to detect these patterns. Results from testing the approach on real-world data show it works better than previous methods. |
Keywords
» Artificial intelligence » Optimization » Prompt
