  Online hate speech is an important issue that breaks the cohesiveness of
online social communities and even raises public safety concerns in our
societies. Motivated by this rising issue, researchers have developed many
traditional machine learning and deep learning methods to detect hate speech in
online social platforms automatically. However, most of these methods have only
considered single type textual feature, e.g., term frequency, or using word
embeddings. Such approaches neglect the other rich textual information that
could be utilized to improve hate speech detection. In this paper, we propose
DeepHate, a novel deep learning model that combines multi-faceted text
representations such as word embeddings, sentiments, and topical information,
to detect hate speech in online social platforms. We conduct extensive
experiments and evaluate DeepHate on three large publicly available real-world
datasets. Our experiment results show that DeepHate outperforms the
state-of-the-art baselines on the hate speech detection task. We also perform
case studies to provide insights into the salient features that best aid in
detecting hate speech in online social platforms.
