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Paper Details

Paper Title
Mitigation of Online Public Shaming Using Machine Learning Framework
In the digital world, currently some of the most far-reaching sites are social media sites on the internet. 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 unrelated information gets spread across the world using online social networking sites. Twitter is one of the most extensive platforms and it is the most popular micro blogging services to connect people with the same interests. Due to the popularity of twitter, it becomes a main target for shaming activities. 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 most of the participating users post comments in a particular occurrence 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 nonshaming in Twitter. The proposed system allows users to find disrespectful words and their overall polarity in percentage is calculated using machine learning algorithm. Shaming tweets are grouped into nine types: abusive, comparison, religious, passing judgment, jokes on personal issues, vulgar, spam, non-spam and whataboutery by choosing appropriate features and designing a set of classifiers to detect it.
Shamers, online user behaviour, public shaming, tweet classification.
Others Details
Paper Id : 61351
Author Name : Mrs.Vaishali Kor
Co-Author Name(s) : Prof. Mrs. D. M. Gohil
Volume/Issue No : Volume 07 Issue 07
Page No : 66-70
DOI Number : DOI:10.21090/IJAERD.61351
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 5.71, SJIF-2017
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406