  Hate speech has grown significantly on social media, causing serious
consequences for victims of all demographics. Despite much attention being paid
to characterize and detect discriminatory speech, most work has focused on
explicit or overt hate speech, failing to address a more pervasive form based
on coded or indirect language. To fill this gap, this work introduces a
theoretically-justified taxonomy of implicit hate speech and a benchmark corpus
with fine-grained labels for each message and its implication. We present
systematic analyses of our dataset using contemporary baselines to detect and
explain implicit hate speech, and we discuss key features that challenge
existing models. This dataset will continue to serve as a useful benchmark for
understanding this multifaceted issue.
