论文标题
将语义和结构信息与图形卷积网络相结合以进行争议检测
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
论文作者
论文摘要
在社交媒体上确定有争议的帖子是开采公众情绪，评估事件影响并减轻两极分化观点的基本任务。但是，现有方法无法进行1）有效地合并了与内容相关的帖子中的语义信息； 2）保留回复关系建模的结构信息； 3）适当处理与培训集中的主题的帖子。为了克服前两个局限性，我们提出了主题 - 局部宣传图卷积网络（TPC-GCN），该网络集成了图形结构和内容，帖子和评论的信息，以进行后级别的争议检测。至于第三个限制，我们将模型扩展到删除的TPC-GCN（DTPC-GCN），以解开与主题相关的和主题无关的功能，然后动态融合。在两个现实世界数据集上进行的广泛实验表明，我们的模型表现优于现有方法。对结果和案例的分析证明，我们的模型可以将语义和结构信息集成在一起，并具有明显的概括性。
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.
