  In the wake of a polarizing election, the cyber world is laden with hate
speech. Context accompanying a hate speech text is useful for identifying hate
speech, which however has been largely overlooked in existing datasets and hate
speech detection models. In this paper, we provide an annotated corpus of hate
speech with context information well kept. Then we propose two types of hate
speech detection models that incorporate context information, a logistic
regression model with context features and a neural network model with learning
components for context. Our evaluation shows that both models outperform a
strong baseline by around 3% to 4% in F1 score and combining these two models
further improve the performance by another 7% in F1 score.
