Stance detection using improved whale optimization algorithm

News is a medium that notifies people about the events that had happened worldwide. The menace of fake news on online platforms is on the rise which may lead to unwanted events. The majority of fake news is spread through social media platforms, since these platforms have a great reach. To identify the credibility of the news, various spam detection methods are generally used. In this work, a new stance detection method has been proposed for identifying the stance of fake news. The proposed stance detection method is based on the capabilities of an improved whale optimization algorithm and a multilayer perceptron. In the proposed model, weights and biases of the multilayer perceptron are updated using an improved whale optimization algorithm. The efficacy of the proposed optimized neural network has been tested on five benchmark stance detection datasets. The proposed model shows better results over all the considered datasets. The proposed approach has theoretical implications for further studies to examine the textual data. Besides, the proposed method also has practical implications for developing systems that can result conclusive reviews on any social problems.