  Abuse on the Internet represents an important societal problem of our time.
Millions of Internet users face harassment, racism, personal attacks, and other
types of abuse on online platforms. The psychological effects of such abuse on
individuals can be profound and lasting. Consequently, over the past few years,
there has been a substantial research effort towards automated abuse detection
in the field of natural language processing (NLP). In this paper, we present a
comprehensive survey of the methods that have been proposed to date, thus
providing a platform for further development of this area. We describe the
existing datasets and review the computational approaches to abuse detection,
analyzing their strengths and limitations. We discuss the main trends that
emerge, highlight the challenges that remain, outline possible solutions, and
propose guidelines for ethics and explainability
