  Toxicity is an increasingly common and severe issue in online spaces.
Consequently, a rich line of machine learning research over the past decade has
focused on computationally detecting and mitigating online toxicity. These
efforts crucially rely on human-annotated datasets that identify toxic content
of various kinds in social media texts. However, such annotations historically
yield low inter-rater agreement, which was often dealt with by taking the
majority vote or other such approaches to arrive at a single ground truth
label. Recent research has pointed out the importance of accounting for the
subjective nature of this task when building and utilizing these datasets, and
this has triggered work on analyzing and better understanding rater
disagreements, and how they could be effectively incorporated into the machine
learning developmental pipeline. While these efforts are filling an important
gap, there is a lack of a broader framework about the root causes of rater
disagreement, and therefore, we situate this work within that broader
landscape. In this survey paper, we analyze a broad set of literature on the
reasons behind rater disagreements focusing on online toxicity, and propose a
detailed taxonomy for the same. Further, we summarize and discuss the potential
solutions targeting each reason for disagreement. We also discuss several open
issues, which could promote the future development of online toxicity research.
