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The datasets most widely used for abusive language detection contain lists ofmessages, usually tweets, that have been manually judged as abusive or not byone or more annotators, with the annotation performed at message level. In thispaper, we investigate what happens when the hateful content of a message isjudged also based on the context, given that messages are often ambiguous andneed to be interpreted in the context of occurrence. We first re-annotate partof a widely used dataset for abusive language detection in English in twoconditions, i.e. with and without context. Then, we compare the performance ofthree classification algorithms obtained on these two types of dataset, arguingthat a context-aware classification is more challenging but also more similarto a real application scenario.

Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection