2023/06/09 by Zecong Wang, Jiaxi Cheng, Wang, Zecong +7 · 1 voice · 7 citations
Computer Science · Medicine · Social Sciences · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #Artificial intelligence #Artificial neural network #COVID-19 diagnosis using AI #Computation and Language (cs.CL) #Computer science #Computer security #Data science #FOS: Computer and information sciences #Fake news #Internet privacy #Misinformation #Misinformation and Its Impacts #Social media #The Internet #Volume (thermodynamics) #World Wide Web #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2306.07401
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/06/09 · arxiv published 2023/06/09 · arxiv updated 2023/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation.