Published Paper Details:

The use of social media has grown exponentially over time with the growth of the Internet and has become the most effective networking platform in the 21st century. However, enhancing social connectivity often creates negative impacts on the society that contribute to a couple of destructive phenomena such as online abuse, harassment, cyberbullying, cybercrime, and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes forces them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumors, and sexual remarks. Therefore, the identification of bullying texts or messages on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Term frequency-inverse text frequency (TFIDF) is used to analyze the accuracy level of four distinct machine learning algorithms.