  Hate speech detection on Twitter is critical for applications like
controversial event extraction, building AI chatterbots, content
recommendation, and sentiment analysis. We define this task as being able to
classify a tweet as racist, sexist or neither. The complexity of the natural
language constructs makes this task very challenging. We perform extensive
experiments with multiple deep learning architectures to learn semantic word
embeddings to handle this complexity. Our experiments on a benchmark dataset of
16K annotated tweets show that such deep learning methods outperform
state-of-the-art char/word n-gram methods by ~18 F1 points.
