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.

新闻是一种通知人们世界范围内发生的事件的媒介。在线平台上虚假新闻的威胁正在上升，这可能会导致意外事件。大多数虚假新闻都是通过社交媒体平台传播的，因为这些平台的影响力很大。为了确定新闻的可信度，通常使用各种垃圾邮件检测方法。在这项工作中，已经提出了一种新的姿势检测方法来识别假新闻的姿势。所提出的姿态检测方法基于改进的鲸鱼优化算法和多层感知器的功能。在提出的模型中，使用改进的鲸鱼优化算法更新了多层感知器的权重和偏差。所提出的优化神经网络的功效已在五个基准姿势检测数据集中进行了测试。所提出的模型在所有考虑的数据集上均显示出更好的结果。所提出的方法对进一步研究文本数据具有理论意义。此外，所提出的方法对于开发可以对任何社会问题进行结论性审查的系统也具有实际意义。