Deep fake news detection system based on concatenated and recurrent modalities

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With the rise in popularity of social media platforms and online forums, they have become a global source of news. Fake News (FNs) spreading through numerous institutions and sectors jeopardizes their reputations, causing users to abandon these platforms. Therefore, there is a huge pool of research in the area of Artificial Intelligence (AI) techniques that are used to detect FNs. Previously, great focus was given to online review classification and free internet posts based on social networks. This research proposes a Deep Learning-based FNs Detection method. This paper proposes a Deep Learning (DL)-based method for detecting FNs. The proposed system consists of three phases; text encoding, feature extraction, and classification. The text encoding process is carried out on the input news words using GLOVE for word representation. The encoded words are then embedded into a specific word length in order to be enrolled in the proposed DL models. The proposed DL models comprise both automatic feature extraction and classification tasks. Furthermore, this study proposes four different DL models, including Convolutional Neural Networks (CNNs) and Concatenated CNNs (C-CNNs), long short-term memory (LSTM), and Gated Recurrent Units, to find an optimal model prior to the section of FNs that outperforms previous works.The proposed DL models are carried out on FNs and FNC datasets which are provided by kaggle, and the suggested C-CNNs algorithm obtained an accuracy of 99.6% and trained faster than others. Multiple evaluation metrics such as precision, recall, F1, and accuracy have been utilized to evaluate the outcome of the proposed models. The experimental results demonstrated overall improvements in the subject of FND when compared with the current models and validated the potential of the proposed methodology for the detection of FNs on Social Media (SM). This study will help researchers to broaden the knowledge of applications of CNNs based on DL methods for FND.