Social network sites involve billions of users around the world wide. User interactions with these social sites, like
twitter have 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 micro blogging services which is generally used to share
unreasonable amount of opinions. In this proposed work automate the task of public shaming detection in Twitter. Shaming
tweets are categorized into nine types: abusive, comparison, passing judgment, religious, jokes on personal issues, vulgar, spam,
non-spam, whataboutery and each tweet is classified into one of these types or as non-shaming. 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.
