An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information Al-Ahmad, Bilal Al-Zoubi, Ala’ M. Abu Khurma, Ruba Aljarah, Ibrahim Fake news COVID-19 Misinformation Evolutionary algorithm Metaheuristics Genetic Algorithm As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy 2021-07-16T09:56:45Z 2021-07-16T09:56:45Z 2021 info:eu-repo/semantics/article Al-Ahmad, B.; Al-Zoubi, A.M.; Abu Khurma, R.; Aljarah, I. An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information. Symmetry 2021, 13, 1091. https://doi.org/10.3390/sym13061091 http://hdl.handle.net/10481/69746 10.3390/sym13061091 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI