  Hate speech is increasingly prevalent online, and its negative outcomes
include increased prejudice, extremism, and even offline hate crime. Automatic
detection of online hate speech can help us to better understand these impacts.
However, while the field has recently progressed through advances in natural
language processing, challenges still remain. In particular, most existing
approaches for hate speech detection focus on a single social media platform in
isolation. This limits both the use of these models and their validity, as the
nature of language varies from platform to platform. Here we propose a new
cross-platform approach to detect hate speech which leverages multiple datasets
and classification models from different platforms and trains a superlearner
that can combine existing and novel training data to improve detection and
increase model applicability. We demonstrate how this approach outperforms
existing models, and achieves good performance when tested on messages from
novel social media platforms not included in the original training data.
