Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection

• URL: http://arxiv.org/abs/2005.07886v1
• Date: Sat, 16 May 2020 06:29:14 GMT
• Title: Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
• Authors: Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang
• Abstract summary: We propose a Topic-Post-Comment Graph Convolutional Network (TPC-GCN) for post-level controversy detection. We extend our model to Disentangled TPC-GCN to disentangle topic-related and topic-unrelated features. Our models can integrate both semantic and structural information with significant generalizability.
• Score: 15.578214777082104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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