The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

Abstract

There are a lot of problems with fake news, which can make people think of things that ‎aren't true. Social media is one of the fastest ways to get information out there because it has ‎a big impact and can manipulate information in both good and bad ways. The goal of this ‎paper is the optimal use of deep learning algorithms to solve the problem of the paper. The research problem is how ‎accurately and to what extent can an individual distinguish between fake news articles using ‎natural language processing and classification algorithms. What are the steps that can be ‎taken to provide a solution?‎‎compared to the previous different methods to solve this problem, including some common ‎deep-learning methods. In this paper, we can find fake news can be found by using the term ‎inverse frequency document (TF-IDF) for feature extraction and a hybrid algorithm of One ‎Dimensional-Convolutional Neural Network (1D-CNN) and Dense as the classifier. The ‎experiments that the proposed dense-based 1D-CNN algorithm substantially outperforms ‎other up-to-date related algorithms with an accuracy of 100%.