A Study on Deep Learning based Cyber-bullying Detection Framework for Online Social Networks

Cyberbullying is the use of technology as a medium to bully someone. Although it has been an issue for man-years, the recognition of its impact on young people has recently increased. Social networking sites provide a fertile medium for bullies, and teens and young adults who use these sites are vulnerable to attacks. Through machine learning, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyberbullying content. Over the last decade, social media has acquired a lot of traction, both positively and negatively way. With the fast growth of social networking, People can communicate with one other via platforms and websites. Directly with no cultural or economic barriers While There have been several advantages to using social media, yet there are none.less negative societal effects One such issue that has arisen is Hate speech has been more prevalent in recent years. Hateful speeches essentially the use of rude and abusive words. using social media It might relate to anybody or something specific. a collection of people who have common interests. In this study, we introduced our approach to dealing with hate speech and, to a considerable part, decreasing it. People express their hatred and rage on social media right instantly, which hurts the sentiments of others. To eradicate hate speech, we dug deep into natural language processing and employed several machine learning models to choose which one to deploy based on accuracy.

Keywords: Natural Language Processing, Classification Technique and Logistic Regression, CNN and Naïve Bayes Classification