Open AccessCyberbullying Detection System Using Machine Learning
Author(s) -
Ms. Shama Kabeer
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38264
Subject(s) - shame , harassment , naive bayes classifier , internet privacy , the internet , psychology , computer science , computer security , social psychology , applied psychology , artificial intelligence , world wide web , support vector machine
Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection