DGCN-TES: Dynamic GCN-Based Multitask Model With Temporal Event Sharing for Rumor Detection

The rumor detection task aims to identify unofficial and unconfirmed information that is spreading on social media. At any given moment, different users express their opinions, focusing on some propagation events, and the posts they make gradually form a social network that expands as it grows. Over time, nodes and edges form a dynamic graph that presents different states at different moments. However, most existing research focuses more on the text content, social context, propagation mode, etc., and they ignore the factors from many aspects and do not consider the dynamic relationships implied in the propagation development of social media. To analyze these dynamic properties, this article proposes a dynamic network-based multitask rumor detection method called dynamic GCN-based multitask model with temporal event sharing for rumor detection (DGCN-TES). This method can effectively capture the dynamic patterns of relationships in propagation events and change them over time to detect rumors. It is mainly divided into three modules: 1) dynamic-graph convolutional network (GCN) module, which uses dynamic graph neural network to construct the propagation graph of rumor events at different times, which can better capture the dynamic spatial features that change over time; 2) content-long short-term memory (LSTM), which uses the LSTM network as a benchmark model and has been improved to better capture time-series text features over time and for multitask shared interactions; and 3) temporal event sharing layer is the sharing layer, which uses time step as the basic unit of sharing, and realizes the sharing interaction between dynamic structural features and temporal text features between the first two modules. We tested the method on two real-world rumor detection datasets PHEME and WEIBO, and the final results show that the method improved F1-score by more than 2.63% and 3.91% compared to the other best baselines baseline.