  Hate speech on social media is a growing concern, and automated methods have
so far been sub-par at reliably detecting it. A major challenge lies in the
potentially evasive nature of hate speech due to the ambiguity and fast
evolution of natural language. To tackle this, we introduce a vectorisation
based on a crowd-sourced and continuously updated dictionary of hate words and
propose fusing this approach with standard word embedding in order to improve
the classification performance of a CNN model. To train and test our model we
use a merge of two established datasets (110,748 tweets in total). By adding
the dictionary-enhanced input, we are able to increase the CNN model's
predictive power and increase the F1 macro score by seven percentage points.
