Curuk, ErenAci, CigdemEssiz, Esra Salac2025-01-062025-01-062018978-1-5386-7893-0https://hdl.handle.net/20.500.14669/21923rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGIn this study, analyzes were performed to detection of cvherhullying by Artificial Neural Network (ANN) based classifiers. In contrast to the general classifiers used in the detection of cyberbullying in the literature, ANN basis classifiers as Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Radial Basis Function (RBF) and Logistic Regression (LR) classifiers have been tested. The performances of the classifiers mentioned in the study were tested with comments from Formspring.me and Myspace media. N-gram model was used for the qualitative derivation and N = 1 was chosen because we wanted to measure the overall performance of the classifiers, also stop-words have been removed from features. In these studies, the F-measure value was taken over than 0.90. Given the accuracy and time performance of the classifiers, it has been observed that the most appropriate classifier for cyherhullying detection is the SGD classifier.trinfo:eu-repo/semantics/closedAccessCyberbullying ClassificationText MiningDVMRBFSGDLogistic RegressionSocial MediaConference Object51WOS:000459847400001N/A