Paper Details

In the digital world, currently some of the most far-reaching sites are social media sites on the internet. Billionsof users are associated with social network sites. User interactions with these social sites, like twitter has an enormous andoccasionally undesirable impact implications for daily life. Large amount of unwanted and unrelated information gets spreadacross the world using online social networking sites. Twitter is one of the most extensive platforms and it is the most popularmicro blogging services to connect people with the same interests. Due to the popularity of twitter, it becomes a main targetfor shaming activities. Nowadays, Twitter is a rich source of human generated information which includes potentialcustomers which allows direct two-way communication with customers. It is noticed that most of the participating users postcomments in a particular occurrence are likely to embarrass the victim. Interestingly, it is also the case that shaming whosefollower counts increase at higher speed than that of the nonshaming in Twitter. The proposed system allows users to finddisrespectful words and their overall polarity in percentage is calculated using machine learning algorithm. Shaming tweetsare 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.

Mrs.Vaishali Kor, Prof. Mrs. D. M. Gohil, “Mitigation of Online Public Shaming Using Machine Learning Framework”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 7, Issue 7, pp. 66-70, July 2020.