  This paper addresses the important problem of discerning hateful content in
social media. We propose a detection scheme that is an ensemble of Recurrent
Neural Network (RNN) classifiers, and it incorporates various features
associated with user-related information, such as the users' tendency towards
racism or sexism. These data are fed as input to the above classifiers along
with the word frequency vectors derived from the textual content. Our approach
has been evaluated on a publicly available corpus of 16k tweets, and the
results demonstrate its effectiveness in comparison to existing state of the
art solutions. More specifically, our scheme can successfully distinguish
racism and sexism messages from normal text, and achieve higher classification
quality than current state-of-the-art algorithms.
