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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.
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.
We train a multi-class classifier to distinguish between these different categories.
Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks.
Some users of social media are spreading racist, sexist, and otherwise hateful content.
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.