Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis.
Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic.
Some users of social media are spreading racist, sexist, and otherwise hateful content.
In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements.
We train a multi-class classifier to distinguish between these different categories.
Consequently, the best monolingual methods perform relatively poorly on code-switched text.
In the wake of a polarizing election, the cyber world is laden with hate speech.