Performance Analysis of Artificial Neural Network Based Classfiers for Cyberbulling Detection

Curuk E., Aci C., Sarac Essiz E.

3rd International Conference on Computer Science and Engineering, UBMK 2018, Sarajevo, Bosnia And Herzegovina, 20 - 23 September 2018, pp.1-5 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk.2018.8566566
  • City: Sarajevo
  • Country: Bosnia And Herzegovina
  • Page Numbers: pp.1-5
  • Keywords: Classification, Cyberbullying, DVM, Logistic Regression, RBF, SGD, Social Media, Text Mining
  • Recep Tayyip Erdoğan University Affiliated: No


In this study, analyzes were performed to detection of cyberbullying 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 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 cyberbullying detection is the SGD classifier.