Hybrid Approach and Architecture to Detect Fake News on Twitter in Real-Time using Neural Networks

Fake news has been a key issue since the dawn of social media. Currently, we are at a stage where it is merely impossible to differentiate between real and fake news. This directly and indirectly affects people's decision patterns and makes us question the credibility of the news shared via social media platforms. Twitter is one of the leading social networks in the world by active users. There has been an exponential spread of fake news on Twitter in the recent past. In this paper, we will discuss the implementation of a browser extension which will identify fake news on Twitter using deep learning models with a focus on real-world applicability, architectural stability and scalability of such a solution. Experimental results show that the proposed browser extension has an accuracy of 86% accuracy in fake news detection. To the best of our knowledge, our work is the first of its kind to detect fake news on Twitter real-time using a hybrid approach and evaluate using real users.