Contextual Hate Speech over Social Media using Machine Learning and Deep Learning

Abstract

With the growing use of social media, information is also increasing dramatically. Because of this growing web material, the disruptions and problems are also increasing. One of the major problems is hate speech, which can cause harmful effects on society, resulting in disputes and chaos. The current automated hate speech detection methods can find the explicit hate very efferently. But now people are using normal words to make hateful content implicit, that is still a challenge for Machine Learning and Deep Learning models. In this research, a dataset is created to detect the context of text in the twister’s hateful tweets targeting others implicitly. Topic modeling is being used for feature extraction and finding out the significance of each word in creating hate. Machine learning and deep learning algorithms along with the proposed feature extraction technique are used to build a context-aware model, that could detect contextual hate speech efficiently.