INTELLIGENT APPROACHES TO HATE DETECTION IN TWEETS

The use of microblogging services like Twitter, Facebook, and Tumblr has increased, making interpersonal contact more indirect and trustworthy; people from various educational and cultural backgrounds interact with their views on several facets of life on a daily basis.These have caused interpersonal difficulties.Consequently, the spread of hate speech issues gets really serious.Hate Speech is when someone uses offensive, violent language.Which focuses on a certain group of people with similar interests a person's gender, caste, religion, color, etc. Manually identifying it is quite time-consuming to gather such content from these websites.The proposed model has the capacity to automatic detection of hate content on Twitter.The proposed methodology is based on a bag of words and the TFIDF (term frequency-inverse document frequency) approach.Machine and deep learning classifiers are trained using these features.The accuracy achieved by the convolutional neural network (CNN) is equivalent to 95% in detecting if a particular tweet is hateful or not in experiments on the already-existing Twitter dataset.