{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:18:20Z","timestamp":1770283100539,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T00:00:00Z","timestamp":1624147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/sym13061091","type":"journal-article","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T21:50:15Z","timestamp":1624225815000},"page":"1091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Bilal","family":"Al-Ahmad","sequence":"first","affiliation":[{"name":"Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan"}]},{"given":"Ala\u2019 M.","family":"Al-Zoubi","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"},{"name":"School of Science, Technology and Engineering, University of Granada, 52005 Granada, Spain"}]},{"given":"Ruba","family":"Abu Khurma","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9265-9819","authenticated-orcid":false,"given":"Ibrahim","family":"Aljarah","sequence":"additional","affiliation":[{"name":"King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sharieh, A., Khurmah, R.A., Masadeh, R., Alzaqebah, A., Alsharman, N., and Sharieh, F. 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