  As the role of online platforms has become increasingly prominent for
communication, toxic behaviors, such as cyberbullying and harassment, have been
rampant in the last decade. On the other hand, online toxicity is
multi-dimensional and sensitive in nature, which makes its detection
challenging. As the impact of exposure to online toxicity can lead to serious
implications for individuals and communities, reliable models and algorithms
are required for detecting and understanding such communications. In this
paper, we define toxicity to provide a foundation drawing social theories.
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.
