  Online platforms have become an increasingly prominent means of
communication. Despite the obvious benefits to the expanded distribution of
content, the last decade has resulted in disturbing toxic communication, such
as cyberbullying and harassment. Nevertheless, detecting online toxicity is
challenging due to its multi-dimensional, context sensitive nature. As exposure
to online toxicity can have serious social consequences, reliable models and
algorithms are required for detecting and analyzing such communication across
the vast and growing space of social media. In this paper, we draw on
psychological and social theory to define toxicity. Then, we provide an
approach that identifies multiple dimensions of toxicity and incorporates
explicit knowledge in a statistical learning algorithm to resolve ambiguity
across such dimensions.
