• Vishakha D. Gawali
• Tushar D.Bhalerao
• Satish G. Kamble

Social network sites involve billions of users around the world wide. User interactions with these social sites, like twitter have a tremendous and occasionally undesirable impact implications for daily life. The major social networking sites have become a target platform for users to disperse a large amount of irrelevant and unwanted information. Twitter, it has become one of the most extravagant platforms of all time and, most popular microblogging services which is generally used to share unreasonable amount of opinions. In this paper, proposed automate the task of public abusive detection in Twitter. It is observed that out of all the participating users who post comments in a particular event, majority of them are likely to humiliate the victim. Interestingly, it is also the abusive whose follower counts increase faster than that of the honored in Twitter. Finally, based on categorization and classification of abusive tweets.

Machine learning, online user behavior, public abusive, tweet classification.

"Abusive language Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.426-431, May-2020, Available :http://www.jetir.org/papers/JETIR2005200.pdf

"Abusive language Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp426-431, May-2020, Available at : http://www.jetir.org/papers/JETIR2005200.pdf