The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation-Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results.

在线社交媒体的快速发展使得滥用语言检测（ALD）成为情感计算领域的热门话题。然而，社交网络中的大多数 ALD 方法都没有考虑用户帖子之间的交互关系，只是将 ALD 视为文本上下文表示学习的任务。为了解决这个问题，我们提出了一种管道方法，该方法同时考虑了帖子的上下文和发布帖子的交互网络的特征。具体来说，我们的方法分为预训练和下游任务。首先，为了捕捉帖子的精细上下文特征，我们使用来自 Transformers (BERT) 的双向编码器表示作为编码器来生成句子表示。之后，我们根据帖子之间的语义相似性以及交互网络结构信息构建一个Relation-Special Network。在此基础上，我们设计了一个关系特殊图神经网络（RSGNN）来在交互网络中传播有效信息并学习文本的分类。实验证明，我们的方法可以有效提高对三个公共数据集的辱骂帖子的检测效果。结果表明，将交互网络结构注入到辱骂性语言检测任务中可以显着提高检测结果。实验证明，我们的方法可以有效提高对三个公共数据集的辱骂帖子的检测效果。结果表明，将交互网络结构注入到辱骂性语言检测任务中可以显着提高检测结果。实验证明，我们的方法可以有效提高对三个公共数据集的辱骂帖子的检测效果。结果表明，将交互网络结构注入到辱骂性语言检测任务中可以显着提高检测结果。