An intelligent feature selection-based fake news detection model for pandemic situation with optimal attention based multiscale densenet with long short-term memory layer

Fake news has recently used the strength and scope of online networking sites to efficiently propagate misinformation, eroding confidence in the press and journalism while also manipulating public perceptions and emotions. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news is so similar to the real ones that it is difficult for humans to identify them. Therefore, Fake News Detection (FND) needs to develop effectual models to overcome the existing challenges. So, in this paper, a novel deep-learning approach is developed for the recognition of fake news in pandemic situations. Initially, text data are collected from benchmark resources related to the pandemic situation and provided to the pre-processing stage. Then, the obtained pre-processed data is inputted into the feature extraction process. Here, the features are extracted using glove embedding, Bidirectional Encoder Representations from Transformers (BERT), and Term Frequency Inverse Document Frequency (TFIDF). Later, the extracted features are taken to the fused optimal weighted feature selection, and the weights are optimized using the Updated Random Variable-based Artificial Rabbits Optimization (URV-ARO), leveraging the Artificial Rabbits Optimization (ARO). The attained optimal weighted features are then given to the classification process. In the classification phase, the fake news is classified with the help of Optimal Attention-based Multiscale Densenet with Long Short-Term Memory layer (OAMDNet-LSTM). Moreover, parameters in DenseNetand LSTM are tuned by developed URV-ARO. Optimizing parameters in the DenseNetand LSTM helps fine-tune the model to achieve higher accuracy in distinguishing between genuine and fake news. The effectiveness of the proposed model is validated with conventional approaches to showcase the effectiveness of others.