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Nowadays, People prefer to follow the latest news on social media, as it ischeap, easily accessible, and quickly disseminated. However, it can spread fakeor unreliable, low-quality news that intentionally contains false information.The spread of fake news can have a negative effect on people and society. Giventhe seriousness of such a problem, researchers did their best to identifypatterns and characteristics that fake news may exhibit to design a system thatcan detect fake news before publishing. In this paper, we have described theFake News Challenge stage #1 (FNC-1) dataset and given an overview of thecompetitive attempts to build a fake news detection system using the FNC-1dataset. The proposed model was evaluated with the FNC-1 dataset. A competitivedataset is considered an open problem and a challenge worldwide. This system'sprocedure implies processing the text in the headline and body text columnswith different natural language processing techniques. After that, theextracted features are reduced using the elbow truncated method, finding thesimilarity between each pair using the soft cosine similarity method. The newfeature is entered into CNN and DNN deep learning approaches. The proposedsystem detects all the categories with high accuracy except the disagreecategory. As a result, the system achieves up to 84.6 % accuracy, classifyingit as the second ranking based on other competitive studies regarding thisdataset.

Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection