The tremendous growth of social media users interacting in online conversations has also led to significant growth in hate speech. Most of the prior works focus on detecting explicit hate speech, which is overt and leverages hateful phrases, with very little work focusing on detecting hate speech that is implicit or denotes hatred through indirect or coded language. In this paper, we present CoSyn, a user- and conversational-context synergized network for detecting implicit hate speech in online conversation trees. CoSyn first models the user's personal historical and social context using a novel hyperbolic Fourier attention mechanism and hyperbolic graph convolution network. Next, we jointly model the user's personal context and the conversational context using a novel context interaction mechanism in the hyperbolic space that clearly captures the interplay between the two and makes independent assessments on the amounts of information to be retrieved from both contexts. CoSyn performs all operations in the hyperbolic space to account for the scale-free dynamics of social media. We demonstrate the effectiveness of CoSyn both qualitatively and quantitatively on an open-source hate speech dataset with Twitter conversations and show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 8.15% - 19.50%.

翻译：社交媒体用户在在线对话中的互动量急剧增长，同时也导致了仇恨言论的显著增加。以往的研究大多聚焦于检测显性仇恨言论——即通过仇恨性短语直接表达的言论，而针对通过间接或编码语言隐含仇恨情绪的隐晦仇恨言论检测的工作则非常有限。本文提出CoSyn——一种融合用户与对话上下文的协同网络，用于在线对话树中的隐晦仇恨言论检测。CoSyn首先利用新型双曲傅里叶注意力机制和双曲图卷积网络，对用户的个人历史与社会上下文进行建模；随后，我们采用双曲空间中的新型上下文交互机制，联合建模用户的个人上下文与对话上下文，清晰捕捉二者之间的相互作用，并独立评估从两种上下文中提取的信息量。CoSyn的所有操作均在双曲空间中执行，以应对社交媒体的无标度动态特性。我们通过基于Twitter对话的开源仇恨言论数据集，从定性与定量两个维度验证了CoSyn的有效性，结果表明CoSyn在隐晦仇恨言论检测中优于所有基线模型，绝对性能提升幅度达8.15%至19.50%。