Abstract
Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news. However, the credibility of the news posted on these platforms has become a significant issue. In other words, written news that contains inaccurate information aiming to mislead readers has been rapidly disseminated on these platforms. In the literature, this news is called fake news. Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news. A hybrid model comprising a recurrent neural network (RNN) and support vector machine (SVM) is incorporated to detect real and fake news. An RNN with bidirectional gated recurrent units was used to encode textual data, including news content and comments, to numerical feature vectors. The encoded features were fed to an SVM with radial basis function kernel to classify the given input of real and fake news. Experiments on the real-world dataset yield encouraging results and demonstrate that the proposed framework outperforms state-of-the-art methods.