Fake News Detection in Social Media using Two-Layers Ensemble Model

With the rapid growth of social media, people can easily access, share and transfer information. Consequently, it enables the wide spread of fake news, hoaxes and rumors along the information. False information misleads people and also gives negative vibes for social, financial and political impacts on everyone around the world. Thus, detecting fake news is essential thing in recent time. To address the issue of fake news detection, most of the researchers have detected fake news by using machine learning algorithms and neural network. But it is still a challenging problem cause of fake news is intentionally created to deceive readers and low accuracy rate in case of the nature of fake news. In this paper, the system is proposed to develop an ensemble classifier for detecting fake news with better accuracy result using the LIAR dataset. According to the experimental results, the proposed two layered ensemble classifier outperforms the existing works by 2.3% in accuracy.