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.- Anthology ID:
- 2020.acl-main.49
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 515–526
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.49
- DOI:
- 10.18653/v1/2020.acl-main.49
- Cite (ACL):
- Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, and Ziang Wang. 2020. Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 515–526, Online. Association for Computational Linguistics.
- Cite (Informal):
- Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection (Zhong et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.acl-main.49.pdf