Social media platforms allow its users to publicly share any kind of content without any restriction. This shared content is available to a very large number of people having access to social media, moreover, it plays a significant role in casting their trust and belief. Due to this, there is an essential necessity to probe the genuineness and authenticity of the publicly shared content. Fake news is one such problem which has recently attracted enormous attention due to its large social, political and economic impacts on an individual and the society. Manual analysis of articles on social media is a cumbersome task and also it does not ensure a high success rate in the detection of fake news. In this article, we proposed a hybrid deep learning architecture to exploit the characteristics of Convolutional Neural Network along with Multilayer Perceptron. To evaluate the architecture, we used LIAR dataset which contains the news text and profile of the news source. After testing the architecture on various models a significant improvement was observed when compared to state of the art models.

Fake News, Deception, Convolutional neural network, Multilayer perceptron

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