Fake News Detection Using Transfer Learning

Abstract

This study employs multi-task transfer learning and natural language processing to ad-dress the issue of identifying fake news (NLP). The work presents a strategy for simulta-neously training a model on various related tasks, such as sentiment analysis and lan-guage modelling, in addition to the false news detection problem. The model must be given the ability to comprehend the structure and patterns seen in natural language in greater depth in order to improve accuracy and efficacy. The study emphasizes the bene-fits of multitask transfer learning for detecting fake news, including increased precision, effectiveness, and adaptability to new data. The method is evaluated against other cut-ting-edge techniques for identifying false news using a dataset of news articles. The outcomes demonstrate that the suggested strategy works better than competing ap- proaches, delivering higher accuracy and quicker compu- tation times. Over all, the study contributes to the field of false news detection by outlining a novel method for enhancing the precision and effectiveness of fake news detection models using mul-titask transfer leam- ing and NLP. The method could be used in a variety of applications, such as social media monitoring and news filtering, as well as other relevant NLP activities