  Hate speech is plaguing the cyberspace along with user-generated content.
This paper investigates the role of conversational context in the annotation
and detection of online hate and counter speech, where context is defined as
the preceding comment in a conversation thread. We created a context-aware
dataset for a 3-way classification task on Reddit comments: hate speech,
counter speech, or neutral. Our analyses indicate that context is critical to
identify hate and counter speech: human judgments change for most comments
depending on whether we show annotators the context. A linguistic analysis
draws insights into the language people use to express hate and counter speech.
Experimental results show that neural networks obtain significantly better
results if context is taken into account. We also present qualitative error
analyses shedding light into (a) when and why context is beneficial and (b) the
remaining errors made by our best model when context is taken into account.
