Safeguarding Online Interaction : Utilizing Machine Learning For Detection of Malicious Intent

Social media platforms are facing a significant challenge with cyberbullying, which is inflicting severe mental health consequences on individuals. To help tackle this, our project is focused on creating computer programs that can identify and prevent cyberbullying on Twitter. We are going to compare three different computer methods: Support Vector Machine (SVM), Naive Bayes, and Bidirectional Long Short-Term Memory (Bi-LSTM) models. We will assess the effectiveness of their performance by utilizing key metrics such as accuracy, precision, and recall. Our main goal is to see if these methods can detect cyberbullying early on. Additionally, we will explore the utilization of Natural Language Processing (NLP) to find threats, hate speech, and other harmful words on social media. On the whole, our project wants to highlight how beneficial computer programs can be in stopping cyberbullying and making social media a safer and more welcoming space for everyone.