  Understanding toxicity in user conversations is undoubtedly an important
problem. Addressing "covert" or implicit cases of toxicity is particularly hard
and requires context. Very few previous studies have analysed the influence of
conversational context in human perception or in automated detection models. We
dive deeper into both these directions. We start by analysing existing
contextual datasets and come to the conclusion that toxicity labelling by
humans is in general influenced by the conversational structure, polarity and
topic of the context. We then propose to bring these findings into
computational detection models by introducing and evaluating (a) neural
architectures for contextual toxicity detection that are aware of the
conversational structure, and (b) data augmentation strategies that can help
model contextual toxicity detection. Our results have shown the encouraging
potential of neural architectures that are aware of the conversation structure.
We have also demonstrated that such models can benefit from synthetic data,
especially in the social media domain.
