  Fake news, rumor, incorrect information, and misinformation detection are
nowadays crucial issues as these might have serious consequences for our social
fabrics. The rate of such information is increasing rapidly due to the
availability of enormous web information sources including social media feeds,
news blogs, online newspapers etc.
  In this paper, we develop various deep learning models for detecting fake
news and classifying them into the pre-defined fine-grained categories.
  At first, we develop models based on Convolutional Neural Network (CNN) and
Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations
obtained from these two models are fed into a Multi-layer Perceptron Model
(MLP) for the final classification. Our experiments on a benchmark dataset show
promising results with an overall accuracy of 44.87\%, which outperforms the
current state of the art.
