  \textbf{Offensive Content Warning}: This paper contains offensive language
only for providing examples that clarify this research and do not reflect the
authors' opinions. Please be aware that these examples are offensive and may
cause you distress.
  The subjectivity of recognizing \textit{hate speech} makes it a complex task.
This is also reflected by different and incomplete definitions in NLP. We
present \textit{hate speech} criteria, developed with perspectives from law and
social science, with the aim of helping researchers create more precise
definitions and annotation guidelines on five aspects: (1) target groups, (2)
dominance, (3) perpetrator characteristics, (4) type of negative group
reference, and the (5) type of potential consequences/effects. Definitions can
be structured so that they cover a more broad or more narrow phenomenon. As
such, conscious choices can be made on specifying criteria or leaving them
open. We argue that the goal and exact task developers have in mind should
determine how the scope of \textit{hate speech} is defined. We provide an
overview of the properties of English datasets from \url{hatespeechdata.com}
that may help select the most suitable dataset for a specific scenario.
