Abstract
With the increased popularity of social media platforms, people are increasingly depending on them for news and updates. Even official media channels post news on social media platforms such as Twitter and Facebook. However, with the vast amount of user-generated content, the credibility of shared information must be verified, and this process should be performed automatically and efficiently to accommodate the huge rate of generated posts. Current technology provides powerful methods and tools to solve the issue of rumor spreading on social networks. In this study, the aim is to investigate the use of state-of-the-art machine learning and deep learning models to detect rumors in a collection of Arabic tweets using the ArCOV19-Rumors dataset. A comprehensive comparison of the performance of the models was conducted. In deep learning experiments, the performances of seven optimizers were compared. The results demonstrated that using over-sampled data did not enhance classical and deep learning models. By contrast, using stacking classifiers increased the predictive model’s performance. As a result, the model became more logical and realistic in predicting rumors, non-rumors, and other classes than using classical machine learning without the stacking technique. Additionally, both long short-term memory (LSTM) and bidirectional-LSTM (Bi-LSTM) with the Root mean square propagation (RMSprop) optimizer obtained the best results. Finally, the results were analyzed to explain and interpret the low performance.