  Detecting online hate is a complex task, and low-performing models have
harmful consequences when used for sensitive applications such as content
moderation. Emoji-based hate is an emerging challenge for automated detection.
We present HatemojiCheck, a test suite of 3,930 short-form statements that
allows us to evaluate performance on hateful language expressed with emoji.
Using the test suite, we expose weaknesses in existing hate detection models.
To address these weaknesses, we create the HatemojiBuild dataset using a
human-and-model-in-the-loop approach. Models built with these 5,912 adversarial
examples perform substantially better at detecting emoji-based hate, while
retaining strong performance on text-only hate. Both HatemojiCheck and
HatemojiBuild are made publicly available. See our Github Repository
(https://github.com/HannahKirk/Hatemoji). HatemojiCheck, HatemojiBuild, and the
final Hatemoji Model are also available on HuggingFace
(https://huggingface.co/datasets/HannahRoseKirk/).
