Fake Information Classifier Using Random Multi-Model Deep Learning

Abstract

Information is a leading factor to determine a nation’s growth and development. Recently the occurrences of Fake and Fraudulent data had increased over all the platforms. Misleading data has resulted in political polarization, decreased trust in public institutions, and undermined democracy. The amount of information is enormous and technological giants are trying to process and eliminate irrelevant information. The complexity of datasets is growing and it requires new methods to process them. Deep learning methods had already surpassed Machine Learning techniques in terms of accuracy and handling of non-linear data. This paper introduces a new classification strategy focused on ensemble deep learning: Via set of deep learning architectures, Random Multi-Model Deep Learning (RMDL) helps to minimise inaccuracy and provides a powerful model to solve traditional deep neural net structures. Evaluation parameters like accuracy, recall score, Micro F1-Score are used to measure its accuracy and precision.