Integrating semantic and structural information with graph convolutional network for controversy detection

18Citations
Citations of this article
150Readers
Mendeley users who have this article in their library.

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 TopicPost-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.

Cite

CITATION STYLE

APA

Zhong, L., Cao, J., Sheng, Q., Guo, J., & Wang, Z. (2020). Integrating semantic and structural information with graph convolutional network for controversy detection. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 515–526). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.49

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free