Exploring the Convergence of Generative AI and Fake News Detection: Technological Advancements and Challenges

With the rapid development of Generative AI technology, the detection and governance of fake news has become a hot topic of concern in academia and society. The wide dissemination of fake news not only disrupts the public's information perception but also triggers social phenomena such as information epidemics, posing significant challenges to social stability, political environment and public health. Especially driven by social media, the speed and breadth of information dissemination have greatly intensified the spread of fake news. The research findings indicate that Generative AI mainly focuses on the optimization of natural language processing and deep learning technologies, providing new technical support for the detection of fake news. Meanwhile, the role of social media platforms and information dissemination pathways have become the focus of scholars' attention. However, issues such as the dissemination of cross-platform fake news, the practical application of generative AI, and the accuracy and robustness of algorithms remain the main challenges in current research. In this regard, this article suggests that future research should further optimize large-scale language models, promote innovation in multimodal fake news detection technology, and strengthen research on cross platform fake news dissemination mechanisms, in order to provide more accurate and effective technical support for fake news governance and alleviate its negative impact on society.