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:社交媒体用户在网上对话中互动的巨大增长也导致仇恨言论的大幅增长。 大多数先前的工作都侧重于发现明显的仇恨言论,这是公开的,利用了仇恨的言词,很少侧重于通过间接或编码语言发现隐含或表示仇恨的仇恨言论。在本文中,我们介绍CoSyn,这是一个用户和谈话-互动的网络,用于在在线对话树中发现隐含的仇恨言论。CoSyn首先使用一个新的双曲四重关注机制和双曲线图解演动网络来模拟用户的个人历史和社会背景。接下来,我们联合模拟用户的个人背景和谈话背景,在双曲线空间使用新的背景互动机制,明确捕捉这两种语言之间的相互作用,并对从两种场合检索的信息数量进行独立评估。CoSyn在超曲线空间中开展所有操作,以考虑社交媒体的无规模动态。我们展示了CoSyn在公开源仇恨言论数据设置上的质量和数量上的有效性,与Twitter对话以及显示COS绝对的改进幅度为815。</s>