Now a days the use of social media has grown exponentially over time with the growth of the Internet and has become the most powerful networking platform in the 21st century. However, theimporvement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment, cybercrime and online trolling. Cyber crime leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Some kind of fake message can impact women to consider as 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, rumours, and sexual remarks. Therefore, the identification of bullying text or message 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.Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), areused to analyse the accuracy level of four distinct machine learning algorithms