Fact Manipulation in News: LLM-Driven Synthesis and Evaluation of Fake News Annotation

Advancements in artificial intelligence and increased internet accessibility have made it simpler to create and disseminate fake news with customized content. However, they also improved the ability to analyze and identify such misinformation. To effectively train high-performance models, we require high-quality, up-todate training datasets. This article delves into the potential for generating fake news through factual modifications of articles. This is facilitated by prompt-based content generated by large language models (LLMs), which can identify and manipulate facts. We intend to outline our methodology, highlighting both the capabilities and limitations of this approach. Additionally, this effort has resulted in new quality synthetic data that can be incorporated into the standard FAK-ES dataset.