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Shaming Tweet Detection Using Machine Learning

Rutuja Nikam, Rutuja Nigade, Neha Pawar, S. B. Shirke

Abstract


Twitter is a social media platform with more than 300 million users and a massive amount of data generated every day with its use. Users should be able to tweet in real time about current events, conditions, thoughts, opinions, or even something completely new. The most important element of Twitter is that there is no system in place to ensure accuracy or dependability, anyone can say whatever they want. This form of Twitter brawl can be an easy technique to attack your opponents while simultaneously giving them the opportunity to attack you. To aid in the discovery of shammers and the shaming of shammers’ tweets, this survey included a range of hate speech detection and machine learning software. Due to the rise of online social networks and an increase in public shaming instances, people are speaking out against site owners' activities. Public shaming on online social networks and related online public forums, such as Twitter, has expanded in recent years. These incidents have been shown to have a negative impact on the victim's social, political, and financial well-being. Despite the obvious harmful repercussions, little has been done to solve this in popular online social media, with the justification that the huge volume and diversity of such comments requires an insurmountable number of human moderators to finish the work. In this study, we automate the process of identifying public shaming via Twitter from the perspective of victims, concentrating on two aspects: incidents and shamers. The six types of humiliating tweets include abuse, comparison, passing judgment, religious/ethnic, sarcasm/joke, and whataboutery, and each tweet falls under one of these.


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References


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