Harnessing RNNs and NLP for News Verification with Chatbot Support

This research tackles the pervasive issue of false news and low information literacy by utilizing a cutting-edge AI system. It helps to reduce the negative impact and exploitation of social media. The advancement in deep learning techniques provides best outcomes for false news detection and news classification categorization. A Recursive Neural Network (RNN) trained on a large dataset of tagged real and fake news stories, natural language processing will be used to analyse news content. Afterwards, the model will forecast the likelihood that an article is false. Furthermore, the system will categorize real news into relevant groups using natural language processing (NLP) techniques. The proposed method is implemented with different datasets and provides an accuracy of 90% it can generalized for providing best outcomes with other datasets. In addition an NLP-powered chatbot has been created to answer user questions on news items. It can able to interpret user queries and provide the most relevant and accurate information by using machine learning. Based on the news analysis from the first component, it will point users to trustworthy sources and offer arguments to balance out any biases.