  Offensive speech detection is a key component of content moderation. However,
what is offensive can be highly subjective. This paper investigates how machine
and human moderators disagree on what is offensive when it comes to real-world
social web political discourse. We show that (1) there is extensive
disagreement among the moderators (humans and machines); and (2) human and
large-language-model classifiers are unable to predict how other human raters
will respond, based on their political leanings. For (1), we conduct a noise
audit at an unprecedented scale that combines both machine and human responses.
For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our
noise audit reveals that moderation outcomes vary wildly across different
machine moderators. Our experiments with human moderators suggest that
political leanings combined with sensitive issues affect both first-person and
vicarious offense. The dataset is available through
https://github.com/Homan-Lab/voiced.
