A Comprehensive Approach to Cyberbullying Classification and Prediction in Social Networks

Author(s)	Jaya suriya A, Chrislyn Easter Dafna S, Jyothikaa K.P, Srinidhi S, Sivakumari S
Country	India
Abstract	This project takes on the pressing issue of cyberbullying by creating a framework that helps classify and predict incidents using Support Vector Machines (SVM), specifically with a Radial Basis Function (RBF), alongside an Intention Model. The approach involves two key phases. First, there's the Group Identification Phase (GIP), where we look for potential risk groups on social networks like Facebook. Then comes the Risk Assessment Phase (RAP), which examines the level of risk for cyberbullying within these identified groups. The SVM-RBF model has proven effective, achieving an impressive accuracy rate of 93% in classifying incidents of cyberbullying. Additionally, the Intention Model offers valuable insights with a solid accuracy of 81%. Overall, this research plays an important role in assessing risks on social networks and provides practical tools that can help make online environments safer.
Keywords	Support Vector Machine, Radial Basis Function
Field	Computer
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-10

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International Journal For Multidisciplinary Research