  With the multiplication of social media platforms, which offer anonymity,
easy access and online community formation, and online debate, the issue of
hate speech detection and tracking becomes a growing challenge to society,
individual, policy-makers and researchers. Despite efforts for leveraging
automatic techniques for automatic detection and monitoring, their performances
are still far from satisfactory, which constantly calls for future research on
the issue. This paper provides a systematic review of literature in this field,
with a focus on natural language processing and deep learning technologies,
highlighting the terminology, processing pipeline, core methods employed, with
a focal point on deep learning architecture. From a methodological perspective,
we adopt PRISMA guideline of systematic review of the last 10 years literature
from ACM Digital Library and Google Scholar. In the sequel, existing surveys,
limitations, and future research directions are extensively discussed.
