Metaheuristic-assisted deep learning model for fake news detection

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ABSTRACT Spreading fake news on social media is regarded as a cybercrime that greatly affects society, government and people. Identifying this fake news by manual analysis is a complex task. Hence, various researchers carried out an analysis on exploiting intelligent approaches to detect fake news. This work aims to propose a hybrid deep learning model for fake news detection. In these works, the news is verified in a unidirectional manner. Hence, there is a demand to change the present scenario and a new model is required for increasing the accuracy of the fake news detection. In this work, the deep learning (DL) model Long Short Term Memory (LSTM) with 3 Parallel-concatenated Convolutional Neural Networks (PCCNN) is utilised for fake news detection. For the feature extraction process, the methods like Term Frequency- Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) are utilised. Finally, for optimising the weights of the neural network, the metaheuristic optimisation Enhanced Sun Flower Optimisation (ESFO) algorithm is used. The robustness of LSTM-PCCNN is compared with other deep learning models to verify its robustness. The experimentation is carried out on the two benchmark datasets and obtained better accuracies of 0.994 on the fake news and 0.997 on the Real and fake news datasets respectively.