  The problem associated with the propagation of fake news continues to grow at
an alarming scale. This trend has generated much interest from politics to
academia and industry alike. We propose a framework that detects and classifies
fake news messages from Twitter posts using hybrid of convolutional neural
networks and long-short term recurrent neural network models. The proposed work
using this deep learning approach achieves 82% accuracy. Our approach
intuitively identifies relevant features associated with fake news stories
without previous knowledge of the domain.
