An Ensemble Approach to Cyberbullying Detection and Prevention on Social Media

Abstract

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has also seen a significant increase which commensurate with the growth of digital technology, and perpetrators hiding behind the cloak of relative internet anonymity. Studies have shown that cyberbullying leaves a lasting psychological scar on its victims and often have devastating outcome. This has necessitated the development of measures to curb cyberbullying. This study presents one of such measure in the form of an ensemble model for cyberbullying detection. The proposed model features a majority voting ensemble approach to cyberbullying detection using three (3) supervised machine learning classifiers: SVM, NB and K-NN, as base learners. The malignant comment dataset, sourced from Kaggle.com. was used for model building at a split ratio of 70: 30 to achieve maximum model training and evaluation respectively. Evaluation result was based on standard metrics. The proposed ensemble model performed best of all the models implemented, with an accuracy of 95%. It was also observed to be the most consistent classifier across all the metrics considered. This showcased the efficacy of the ensemble model in cyberbullying comments detection.