Author : Mrs.Vaishali Kor and Prof. Mrs. D.M.Gohil

In the digital world, billions of users are associated with social network sites. User interactions with these social sites, like twitter has an enormous and occasionally undesirable impact implications for daily life. Large amount of unwanted and irrelevant information gets spread across the world using online social networking sites. Twitter has become one of the most enormous platforms and it is the most popular micro blogging services to connect people with the same interests. Nowadays, Twitter is a rich source of human generated information which includes potential customers which allows direct two-way communication with customers. It is noticed that out of all the participating users who post comments in a particular occurrences, majority of them are likely to embarrass the victim. Interestingly, it is also the case that shaming whose follower counts increase at higher speed than that of the non-shaming in Twitter. The proposed system allows users to find disrespectful words and their overall polarity in percentage is calculated using machine learning. Shaming tweets are grouped into nine types: abusive, comparison, religious, passing judgment, jokes on personal issues, vulgar, spam, nonspam and whataboutery by choosing appropriate features and designing a set of classifiers to detect it.

Keywords: Shamers, online user behaviour, public shaming, tweet classification.