Detecting Cyber Bullying on Twitter using Support Vector Machine

On the platform provided by social media, a significant number of young people are subjected to bullying. Cyberbullying is becoming a more widespread problem with the proliferation of social networking platforms. To discover word similarities in the tweets that were written by bullies, utilize Machine Learning, and develop a ML model that can do automatic identification of the bullying acts on different social media websites or platforms. Yet, many other methods for detecting bullying on social media have been deployed, however, the majority of these methods are text-based. The goal of this work is to show how software may be developed that can detect bullying in tweets, postings, and other online communications. It has been suggested to use a model that uses machine learning to identify bullying on Twitter. SVM is used in the classification process, whereas NLP is utilized for the processing of the data. Additionally, in order to decide whether or not a tweet constitutes bullying, the Twitter API is used to gather the tweets, which are then loaded into a model.