CYBER BULLYING DETECTION: AN ENSEMBLE BASED MACHINE LEARNING APPROACH

Abstract

Research on cyberbullying detection.To identify control and reduce the bullying contents spread over social media sites. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms. Thus there is an extensive need to identify, control and reduce the bullying contents spread over social media sites, which has motivated us to conduct this research to automate the detection process of offensive language or cyberbullying. In our work, Logistic Regression and Bagging ensemble model classifier have performed individually best in detecting cyberbullying which has been outperformed by our proposed SLE and DLE voting classifiers. Our proposed SLE and DLE models yield the best performance of 96% when TF-IDF (Unigram) feature extraction is applied with K-Fold cross-validation.