An Improved and Optimized Gated Recurrent Unit and Long Short-Term Memory Model for Fake News Detection

Abstract

This study presents a novel approach for detecting counterfeit news, employing an advanced hybrid model that integrates Enhanced Gated Recurrent Unit and Long Short-Term Memory networks, termed as IGRU-LSTM. Initially, the database is assembled from the Information Security and Object Technology (ISOT) database and Wikipedia databases. From the database, the real and fake news is detected by considering news reviews. The dataset may contain unwanted information line URLs and symbols, which should be corrected to achieve efficient fake news detection. So, the pre-processing technique should be considered such as special symbol removal, URLS removal, upper to lower case conversion and replace contractions. After that, the pre-processed data is sent to the embedding procedure for word embedding. Finally, the IGRU-LSTM classifier is utilized for classifying real and fake news detection. In the combined GRU-LSTM framework, we incorporate the Enhanced Wild Horse Optimisation (EWHO) algorithm to optimize the selection of optimal weighting parameters. We utilize MATLAB for implementing this method. To evaluate the effectiveness of our approach, we analyze key performance metrics like precision, recall, and accuracy, and compare them with established methods including CNN-PSO, CNN-FO, and standard CNN.