FN-Net: A Deep Convolutional Neural Network for Fake News Detection

Information and communication technology has evolved rapidly over the past decades, with a substantial development being the emergence of social media. It is the new norm that people share their information instantly and massively through social media platforms. The downside of this is that fake news also spread more rapidly and diffuse deeper than before. This has caused a devastating impact on people who are misled by fake news. In the interest of mitigating this problem, fake news detection is crucial to help people differentiate the authenticity of the news. In this research, an enhanced convolutional neural network (CNN) model, referred to as Fake News Net (FN-Net) is devised for fake news detection. The FN-Net consists of more pairs of convolution and max pooling layers to better encode the high-level features at different granularities. Besides that, two regularization techniques are incorporated into the FN-Net to address the overfitting problem. The gradient descent process of FN-Net is also accelerated by the Adam optimizer. The empirical studies on four datasets demonstrate that FN-Net outshines the original CNN model.