Cyberbullying Detection on Social Media Using Machine Learning Techniques

Abstract

With the advancement of social media, cyberbullying among social media users has grown knowingly, causing serious problems such as economic, health, social, and psychological problems. Nowadays, most of the social media users have been affected by cyberbullying, aggressive behavior, and suffering from digital harassment. Several machine-learning techniques are applied to detect cyberbullying from text data sets generated by Twitter and Formspring.com. It is found that cyber-bullies participate in fewer online groups and are not much more popular than normal users. However, one major issue in cyberbullying detection and sentiment analysis is the deficiency of labeled data. Consequently, researchers are usually forced to depend on survey-based data where committers and sufferers provide details about their imitations. Therefore, un-labeled data is gathered, and data annotation techniques are applied to label the data set. After that two more labeled datasets are gathered and machine learning classifiers (voting classifier, random forest, SVM, linear SVM, and Gaussian naïve Bayes) are applied to these labeled datasets to train the model. Thus, the trained model was applied to the primary dataset, but the results gained were not satisfactory. Therefore, the select-k-best (chi^2) feature selection algorithm was applied to reduce features. Hence, the primary dataset is verified by applying the trained model with an accuracy of 91%.