Fake News Identification on Twitter with Hybrid CNN and RNN Models
This addresses the growing problem of fake news propagation for social media users and platforms, but it is incremental as it combines existing deep learning methods.
The authors tackled fake news detection on Twitter by proposing a hybrid CNN-RNN framework, achieving 82% accuracy in classification.
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.