Global Structural-Temporal Graph Network with Public Opinion for Online Rumor Detection

Tiening Sun, Zhong Qian, Peifeng Li, Qiaoming Zhu

Abstract: Rumors on social media can spread rapidly and widely with the help of the Internet characteristics, causing serious negative impacts on social stability and public life. In order to distinguish rumors from non-rumors, most of the existing methods are based on neural units to encode and observe the content of claims, user comments and rumor propagation patterns. However, these methods only consider the event context information in a single conversation thread, ignoring the public opinion (global contextual information) corresponding to the event in the external news environment. Be aware that users are easily distracted by opinion leaders to false facts and induced to make supportive replies on false claims. In order to address the above-mentioned limitation, we propose a Global Structural-Temporal Graph Network (GSTGN) framework. Specifically, we first construct a multi-modal global opinion graph based on the conversation threads belonging to the same event to capture the external public opinion of the target event. Then to enhance representation learning, we design a Structural-Temporal (ST) unit to encode structural and temporal features of the local conversation graph, and utilize the structural feature of the local graph to guide the learning and encoding of the global opinion graph. Experimental results on two public benchmark datasets prove that our GSTGN method achieves better results than other state-of-the-art models.