Fake News Detection Using Feature Selection and Deep Learning

Abstract

Abstract: The days have come when people are increasingly using apps on their smart devices. Every day everyone generates millions of data per second. As for usage increases, so soon negative actions such as rumors and false stories on social media also increase. To resolve this issue, we have proposed a "Fake News Detection using Feature Selection and Deep Learning" feature. To develop an automated rumor detection model, this study prefers a hybrid model to separate rumors using an Deep Learning (Convolution Neural Network) and a filter-wrapper (Artificial Bee Colony) classified by the Naïve Bayes classifier as a test algorithm. The feature set uses a mixed model with CNN as an Deep learning model and ABC as a training data filter model and the results are applied to the Naïve Bayes classifier to calculate accuracy.