THE EXPERIMENTAL CASE OF CYBERBULLYING DETECTION IN THE SOCIAL NETWORK
Keywords:Cyber bullying, Victims, Machine Learning, Natural Language Processing, Twitter, Natural Language Processing
Cyberbullying is a ubiquitous social practice that can have a variety of severe psychological, behavioral, and health consequences for cyberbullying victims, according to this investigation. According to studies, cyberbullying happens all throughout the world. Understanding the characteristics and processes that lead to cyberbullying is crucial for treatments aimed at decreasing antisocial behavior online. Cyber bullying is a widespread problem on the internet that affects both teens and adults. It has resulted in misfortunes such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work combines data from two types of cyber bullying, hate speech tweets from Twitter and comments from Wikipedia forums focused on personal assaults, to create a model for detecting cyberbullying in text data using Natural Language Processing and Machine Learning. To determine the optimum technique, three feature extraction approaches and four classifiers are investigated.