Ensemble Learning with optimum Feature Selection for Tweet Fake News Detection using the Dragonfly approach

The rise of bogus news on social media necessitates robust detection techniques. This is especially important in Arabic-speaking online communities, where disinformation is common. In this study, Dragonfly-based feature selection and ensemble learning models are used to detect Arabic fake news on Twitter. Based on the concept of dragonfly foraging, the suggested method intelligently selects and prioritizes key elements in Arabic tweets. This intentional feature selection improves feature discrimination and computational efficiency. Additionally, ensemble learning models analyze the selected attributes cooperatively. The ensemble technique in this paper comprises four models: decision tree, XGBoost, gradient boosting, and LGBM, to enhance detection accuracy and robustness by utilizing these classification algorithms. Empirical tests on a large Arabic tweet dataset demonstrate the effectiveness of the methodology. The Dragonfly-based feature selection method highlights the most informative traits, thereby improving fake news detection accuracy. Ensemble learning models are more resilient to noisy and deceptive data, enhancing the system’s ability to identify fake news. Dragonfly-based feature selection and ensemble learning models enhance the identification of Arabic fake news. This approach contributes to facilitating more credible and well-informed digital dialogue within Arabic-speaking online communities, extending beyond its immediate applications. The results show that improving the ensemble models using the Dragonfly algorithm achieved the highest accuracy of 90.55 when employing XGBoost. This paper provides powerful tools to combat misinformation and promote the spread of factual information as false news continues to evolve.