A Preventive Measure on Hate Speech Detection On Online Social Network using Naïve Bayes

EasyChair Preprint 2967

Abstract

Abstract- Online Social network sites are a perfect vicinity for net users to hold in touch, proportion information about their day by day activities and pursuits, publishing and having access to documents, photos and videos. OSN like Facebook, Twitter, Instagram and Google give users the freedom to express their thoughts in text without following traditional language grammar, thereby making it difficult to mine social media for insights. Nevertheless, online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, or may create silence among platform users, and some of it can incite other users to commit violence. Where we have developed a model in which will perform detection on the comments posted on a post based on certain criterions of words distinguishing them whether they are vulgar,offensive or hateful Also a contribution to this will add more functionality to the project. This paper proposes a model which will detect hateful words using Naive Bayes and prevent the hateful comments by hiding them by using hiding mechanism. This work is important because only detecting and identifying hate words is not enough, some actions must be taken against those hateful comments.

Keyphrases: Hateful, Naive Bayes, OSN

@booklet{EasyChair:2967,
  author    = {Nupur Khond and Godawari Padwal and Veena Ulgekar and Tejaswini Parsekar and Sumit Harale},
  title     = {A Preventive Measure on Hate Speech Detection On Online Social Network using Naïve Bayes},
  howpublished = {EasyChair Preprint 2967},
  year      = {EasyChair, 2020}}