A Deep Ensemble Framework for Fake News Detection and Classification
This addresses the problem of misinformation detection for social media and online news users, but it is incremental as it builds on existing deep learning methods.
The paper tackled fake news detection and classification by developing deep learning models, achieving an overall accuracy of 44.87% that outperforms the current state of the art.
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.