An Efficient Model for Fake News Detection Using Transformer

The public discourse is seriously threatened by the massive circulation of false information via social media and other platforms. This study investigates the potential of transformer models to effectively identify fake news, comparing their performance to mores Deep Learning (DL) and Machine Learning (ML) approaches. While DL and ML models have shown promise in this domain, they often struggle to capture the nuances of language context. Transformers, a more recent advancement indeep learning, excel at understanding the contextual relationships within text. By training transformer models on real-world datasets and evaluating their accuracy in identifying fake news, The objective of this study is to aid in the advancement of false news identification systems that are more dependable and precise. Through a critical analysis of transformer models in comparison to existing techniques, this study seeks to pave the way for future advancements in natural language processing (NLP) and the creation of a more trustworthy online information ecosystem.