Severity Detection of Cyberbullying in Online Social Networks Using Machine Learning
Madhura Vikram Vyawahare, Sharvari S. Govilkar · 2022
Cyberbullying is one of the negative corollary of advancement in technology which results in the serious national health issue especially among children. It leaves significant psychological impressions which may also lead the victim to suicidal attempts in extreme cases and hence detection of cyberbullying is getting a lot of attention in recent research. Most previous studies have focused on detection of cyberbullying which still can't prevent the impact it creates on minds which is difficult to wipe off. Detection and prediction of cyberbullying is the need of time in online social networks. This work presents the proposed system for prediction of cyberbullying and identifying the nature of the post using multiclass and multilabel classification. The experimental comparative examination and analysis is also included for state of the art classification algorithms which are proven to be giving better results in other domains combined with BERT Vectorization.