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

本研究通过给出上下文，重新注释部分英文侮辱语言检测数据集，比较了基于上下文和不基于上下文两种数据集上三种分类算法的性能，发现基于上下文的分类更具挑战性，但也更类似于实际应用场景。