[Submitted on 29 Jan 2023 (v1), last revised 3 Feb 2023 (this version, v2)]
Abstract: This paper examines social web content moderation from two key perspectives:
automated methods (machine moderators) and human evaluators (human moderators).
We conduct a noise audit at an unprecedented scale using nine machine
moderators trained on well-known offensive speech data sets evaluated on a
corpus sampled from 92 million YouTube comments discussing a
