The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech, affecting people from various demographics. 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 context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. CoSyn introduces novel ways to encode these external contexts and employs a novel context interaction mechanism that clearly captures the interplay between them, making independent assessments of the amounts of information to be retrieved from these noisy contexts. Additionally, it carries out all these operations in the hyperbolic space to account for the scale-free dynamics of social media. We demonstrate the effectiveness of CoSyn on 6 hate speech datasets and show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.

翻译：社交媒体用户在线对话交互的急剧增长导致仇恨言论大幅增加，影响了不同人口群体的用户。以往研究主要关注显式仇恨言论的检测（即直接使用仇恨性短语的明显表达），而针对通过间接或编码语言表达仇恨的隐式仇恨言论的检测工作极少。本文提出CoSyn——一种上下文协同神经网络，通过显式融入用户上下文和对话上下文来检测在线对话中的隐式仇恨言论。CoSyn创新性地编码这些外部上下文，并采用新型上下文交互机制清晰捕捉二者间的相互作用，独立评估应从这些嘈杂上下文中提取的信息量。此外，所有操作均在双曲空间中执行，以契合社交媒体的无尺度动态特性。我们在6个仇恨言论数据集上验证了CoSyn的有效性，结果表明CoSyn在隐式仇恨言论检测中全面超越基线模型，绝对提升幅度达1.24%-57.8%。