  Hate speech detection is a critical problem in social media platforms, being
often accused for enabling the spread of hatred and igniting physical violence.
Hate speech detection requires overwhelming resources including
high-performance computing for online posts and tweets monitoring as well as
thousands of human experts for daily screening of suspected posts or tweets.
Recently, Deep Learning (DL)-based solutions have been proposed for automatic
detection of hate speech, using modest-sized training datasets of few thousands
of hate speech sequences. While these methods perform well on the specific
datasets, their ability to detect new hate speech sequences is limited and has
not been investigated. Being a data-driven approach, it is well known that DL
surpasses other methods whenever a scale-up in train dataset size and diversity
is achieved. Therefore, we first present a dataset of 1 million realistic hate
and non-hate sequences, produced by a deep generative language model. We
further utilize the generated dataset to train a well-studied DL-based hate
speech detector, and demonstrate consistent and significant performance
improvements across five public hate speech datasets. Therefore, the proposed
solution enables high sensitivity detection of a very large variety of hate
speech sequences, paving the way to a fully automatic solution.
