论文标题
仇恨言论和反语音检测：对话环境确实很重要
Hate Speech and Counter Speech Detection: Conversational Context Does Matter
论文作者
论文摘要
仇恨言论与用户生成的内容一起困扰网络空间。本文调查了对话环境在在线仇恨和反语音的注释和检测中的作用，在对话线程中，上下文定义为对话线程中的前面评论。我们创建了一个上下文感知的数据集，用于在reddit评论上进行三向分类任务：仇恨言语，反语音或中立。我们的分析表明，上下文对于识别仇恨和反语音至关重要：大多数评论的人类判断都会根据我们是否向注释者展示上下文而改变。语言分析吸引了人们用来表达仇恨和反语言的语言的见解。实验结果表明，如果考虑到上下文，神经网络将获得明显更好的结果。我们还提出了定性错误分析，将灯光放到（a）何时以及为什么有益的情况下以及（b）考虑到上下文时我们最佳模型造成的其余错误。
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.
