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

arXiv: https://arxiv.org/abs/2005.07886 · HTML: https://mlsanity.com/paper/2005.07886/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 8 of 8)

- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks](https://mlsanity.com/paper/2403.16033/) (2024)
- [Controversy Detection: a Text and Graph Neural Network Based Approach](https://mlsanity.com/paper/2112.11445/) (2021)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)
- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)
- [From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework](https://mlsanity.com/paper/2605.02939/) (2026)

## Abstract (arXiv)

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.