Essiz, Esra SaracOturakci, Murat2025-01-062025-01-0620210010-46201460-206710.1093/comjnl/bxaa0662-s2.0-85105426932https://doi.org/10.1093/comjnl/bxaa066https://hdl.handle.net/20.500.14669/2193As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.eninfo:eu-repo/semantics/closedAccesscyberbullyingartificial bee colonyfeature selectionclassificationArticle3133Q230564WOS:000644547300004Q3