  The spread of hate speech on social media space is currently a serious issue.
The undemanding access to the enormous amount of information being generated on
these platforms has led people to post and react with toxic content that
originates violence. Though efforts have been made toward detecting and
restraining such content online, it is still challenging to identify it
accurately. Deep learning based solutions have been at the forefront of
identifying hateful content. However, the factors such as the context-dependent
nature of hate speech, the intention of the user, undesired biases, etc. make
this process overcritical. In this work, we deeply explore a wide range of
challenges in automatic hate speech detection by presenting a hierarchical
organization of these problems. We focus on challenges faced by machine
learning or deep learning based solutions to hate speech identification. At the
top level, we distinguish between data level, model level, and human level
challenges. We further provide an exhaustive analysis of each level of the
hierarchy with examples. This survey will help researchers to design their
solutions more efficiently in the domain of hate speech detection.
