Propagation tree says: dynamic evolution characteristics learning approach for rumor detection

Abstract

Due to the rapid spread of rumors on social media, which has a detrimental effect on our lives, it is becoming increasingly important to detect rumors. It has been proved that the study of dynamic graphs is helpful to capture the temporal change of information transmission and understand the evolution trend and pattern change of events. However, the dynamic learning methods currently studied do not fully consider the interaction characteristics of the evolutionary process. Therefore, it is difficult to fully capture the structural and semantic differences between them. In order to fully exploit the potential correlations of such temporal information, we propose a novel model named dynamic evolution characteristics learning (DECL) method for rumor detection. First, we partition the temporal snapshot sequences based on the propagation structure of rumors. Secondly, a multi-task graph contrastive learning method is adopted to enable the graph encoder to capture the essential features of rumors, and to fully explore the temporal structural differences and semantic similarities between true rumor and false rumor events. Experimental results on three real-world social media datasets confirm the effectiveness of our model for rumor detection tasks.