Fighting Fake News with Synthetic Data
Using LLMs to Generate and Detect Manipulated Facts
This research introduces a novel methodology for generating high-quality synthetic fake news to improve detection systems by manipulating specific facts while maintaining article coherence.
- Extracts key facts from real articles and strategically modifies them to create realistic fake content
- Proposes new evaluation metrics: coherence, dissimilarity, and correctness
- Demonstrates how synthetic data can strengthen fake news classification systems
- Addresses critical information security concerns in the digital media landscape
The approach offers significant security benefits by enabling better training of detection systems against sophisticated misinformation, helping organizations protect information integrity in an era of AI-generated content.