结合语义和结构信息的图卷积网络控制检测
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
摘要：
识别社交媒体上有争议的帖子是挖掘公众情绪，评估事件影响，缓解两极分化的基本任务。然而，现有的方法不能1）有效地结合来自内容相关的帖子的语义信息; 2）保留用于回复关系建模的结构信息; 3）适当地处理来自与训练集中的主题不相似的主题的帖子。为了克服前两个限制，我们提出了主题后评论图卷积网络（TPC-GCN），它集成了来自图结构和主题，帖子和评论的内容的信息，用于后一级的争议检测。对于第三个限制，我们将我们的模型扩展到解纠缠TPC-GCN（DTPC-GCN），以解开主题相关和主题无关的功能，然后动态融合。在两个真实数据集上的大量实验表明，我们的模型优于现有的方法。结果和实例分析表明，该模型能够有效地整合语义和结构信息，具有较强的泛化能力。
英文摘要：
Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views. However, existing methods fail to 1) effectively incorporate the semantic information from content-related posts; 2) preserve the structural information for reply relationship modeling; 3) properly handle posts from topics dissimilar to those in the training set. To overcome the first two limitations, we propose Topic-Post-Comment Graph Convolutional Network (TPC-GCN), which integrates the information from the graph structure and content of topics, posts, and comments for post-level controversy detection. As to the third limitation, we extend our model to Disentangled TPC-GCN (DTPC-GCN), to disentangle topic-related and topic-unrelated features and then fuse dynamically. Extensive experiments on two real-world datasets demonstrate that our models outperform existing methods. Analysis of the results and cases proves that our models can integrate both semantic and structural information with significant generalizability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
