Stacked Layer Based Deep Learning Approach for Fake News Classification

The growth of the global population”s social., economic., and emotional spheres is impacted by fake news and misleading information. Several classification systems are available to help identify fake news., but they needed to be quicker., more secure., and as trustworthy as was required at the time. Since manually confirming the integrity of news is difficult and expensive., researchers have shown much interest in this area. Different techniques for spotting fake news., including sentiment-based., image-based., content-based., social context-based., and hybrid context-based categorization., were examined. In this paper., we have developed a lightweight Deep Learning (DL) network for Fake News Classification (FNC)., a sentiment-based classification. The deep network consists of a stacked layer based feature extraction approach. Here stack layer first extracts the global features by the network of Bidirectional-Long Short Term Memory (Bi-LSTM) and Bidirectional-Gated Recurrent Unit (Bi-GRU). The results of the global features we have extracted the local features by the Convolutional Neural Network (CNN). The experiment findings show that the suggested lightweight DL network is more successful in producing superior results than the other methods.