Title: Cyberbullying detection: an ensemble learning approach
Authors: Pradeep Kumar Roy; Ashish Singh; Asis Kumar Tripathy; Tapan Kumar Das
Addresses: Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat, India ' School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
Abstract: Online social networking platforms have become a common choice for people to communicate with friends, relatives, or business partners. This allows sharing life achievement, success, and much more. In parallel, it also invited hidden issues such as web-spamming, cyberbullying, cybercrime, and others. This paper addresses the issue of cyberbullying using an ensemble machine learning model. The complete experiment works in two phases: firstly, k-nearest neighbour, logistic regression and, decision tree classifiers are used to detect the bullying post. Secondly, the prediction outcomes of these classifiers are passed to a voting-based ensemble learning model for the predictions. The experimental outcomes confirmed that the ensemble model is detecting bullying posts with good accuracy.
Keywords: cyberbullying; Twitter; social network; ensemble learning; classification.
International Journal of Computational Science and Engineering, 2022 Vol.25 No.3, pp.315 - 324
Received: 29 Apr 2021
Accepted: 31 Jul 2021
Published online: 30 May 2022 *