Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection (2020)

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.

## introduction social media such as reddit 1 and chinese weibo 2 has been the major channel through which people can easily propagate their views. in the open and free circumstance, the views expressed by the posts often spark fierce discussion and raise controversy among the engaging users. these controversial posts provide a lens of public sentiment, which bring about several tasks such as news topic selection, influence assessment @xcite , and alleviation of polarized views @xcite . as a basis of all mentioned tasks, automatically identifying the controversial posts has they two obviously use different techniques. xiaomi's mimoji is automatically generated while apple's memoji is hand-made. thus, xiaomi obviously do not copy. ## methodology in this section, we introduce the topic-post-comment graph convolutional network (tpc-gcn) and its extension disentangled tpc-gcn (dtpc-gcn), as shown in figure 2 . we first introduce the tpc graph construction and then detail the two models. ## experiment in this section, we conduct experiments to compare our proposed models and other baseline models. specifically, we mainly answer the following evaluation questions: eq1: are tpc-gcn and dtpc-gcn able to improve the performance of controversy detection? eq2: how effective are different information in tpc-gcn, including the content of topics, posts, and comments as well as the topic-post-comment structure? eq3: can dtpc-gcn learn disentangled features and dynamically fuse them for controversy detection? ## conclusion in this paper, we propose a novel method tpc-gcn to integrate the information from the graph structure and content of topics, posts, and comments for post-level controversy detection on social media. unlike the existing works, we exploit the information from related posts in the same topic and the reply structure for more effective detection. to improve the performance of our model for inter-topic detection, we propose an extension of tpc-gcn named dtpc-gcn, to disentangle the topic-related and topic-unrelated features and then dynamically fuse them. extensive experiments conducted on two datasets demonstrate that our proposed models outperform the compared methods and prove that our models can integrate both semantic and structural information with significant genaralizablity.