2026-06-11T00:03:37.225Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9131 2022-10-06T09:48:23Z 3

Novel Chaotic Best Firefly Algorithm: COVID-19 Fake News Detection Application, Chapter in SCI Studies in Computational Intelligence: Advances in Swarm Intelligence, Springer, volume 1054 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9131 https://link.springer.com/chapter/10.1007/978-3-031-09835-2_16 M. Zivkovic A. Petrovic K. Venkatachalam I. Strumberger H. Shaker Jassim N. Bacanin The COVID-19 pandemic period is approaching the two-year mark of its lifetime. During the pandemic, the people experienced various restrictions and different problems were raised unlike before. While the people stayed in their homes, electronic device usage had a decisive spike. This increased time spent on the internet and the amount of misinformation followed the same trend. The problem with the information on the COVID-19 is that there are too many different sources presenting different data. While some are just trying to help and do not have the latest or the most accurate information, some sources are malicious. Either way, everything but the latest and the most accurate data needs to be filtered when regarding a serious matter as such. The research proposed in this manuscript is aimed to identify COVID-19 fake news by performing wrapper-based feature by using an improved version of the well-known firefly algorithm. Practical simulations were done against a well-known dataset used in the domain of this problem, Koirala. The proposed method managed to achieve high accuracy of classification by using a smaller number of features in comparison with the state-of-the-art methods tested in the same experimental environment. bookPart Springer, Cham 285 305 10.1007/978-3-031-09835-2_16 SCI Studies in Computational Intelligence: Advances in Swarm Intelligence, volume 1054