A Novel IN-Gram Technique for Improving the Hate Speech Detection for Larger Datasets

Abstract

Hate speech is a type of written or a spoken statement that is used to demean or humiliate a person or a community. In this era of new age socialism, this type of speech is prevalent on social media platforms, where certain groups of people display offensive behaviour towards some people that may be distributed over gender, religion, nationality, etc. These kinds of activities must be avoided or suspended on social media platforms. Therefore, it is necessary to automate the detection of hateful content that gets circulated on the social media. The research work provides an enhanced technique as compared to the existing techniques with improved performance. The proposed model of IN-Gram compares the performance of detection of hateful content on social media with the traditional TF-IDF, N-Gram and PMI techniques. The proposed approach improves the hate speech detection rate by 10–12% for larger datasets as compared to existing approaches.