  Today, the internet is an integral part of our daily lives, enabling people
to be more connected than ever before. However, this greater connectivity and
access to information increase exposure to harmful content such as
cyber-bullying and cyber-hatred. Models based on machine learning and natural
language offer a way to make online platforms safer by identifying hate speech
in web text autonomously. However, the main difficulty is annotating a
sufficiently large number of examples to train these models. This paper uses a
transfer learning technique to leverage two independent datasets jointly and
builds a single representation of hate speech. We build an interpretable
two-dimensional visualization tool of the constructed hate speech
representation -- dubbed the Map of Hate -- in which multiple datasets can be
projected and comparatively analyzed. The hateful content is annotated
differently across the two datasets (racist and sexist in one dataset, hateful
and offensive in another). However, the common representation successfully
projects the harmless class of both datasets into the same space and can be
used to uncover labeling errors (false positives). We also show that the joint
representation boosts prediction performances when only a limited amount of
supervision is available. These methods and insights hold the potential for
safer social media and reduce the need to expose human moderators and
annotators to distressing online messaging.
