  Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting sexist and racist languages. With a public English Twitter corpus of
20 thousand tweets in the type of sexism and racism, our approach shows a
promising performance of 0.827 F-measure by using HybridCNN in one-step and
0.824 F-measure by using logistic regression in two-steps.
