Abstract

Abstract: - 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 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 and what aboutery, 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. Interestingly, it is also the shaming whose follower counts increase faster than that of the non-shaming in Twitter.Keywords: Remove Shammers, online user behavior, public shaming, tweet classification.