  Hate speech detection has become a hot topic in recent years due to the
exponential growth of offensive language in social media. It has proven that,
state-of-the-art hate speech classifiers are efficient only when tested on the
data with the same feature distribution as training data. As a consequence,
model architecture plays the second role to improve the current results. In
such a diverse data distribution relying on low level features is the main
cause of deficiency due to natural bias in data. That's why we need to use high
level features to avoid a biased judgement. In this paper, we statistically
analyze the Perspective Scores and their impact on hate speech detection. We
show that, different hate speech datasets are very similar when it comes to
extract their Perspective Scores. Eventually, we prove that, over-sampling the
Perspective Scores of a hate speech dataset can significantly improve the
generalization performance when it comes to be tested on other hate speech
datasets.
