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

Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang
2020 arXiv   pre-print
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
more » ... ent 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.
arXiv:2005.07886v1 fatcat:m7hpjisvl5f7nfy6xruihtrpgi