SSRI-Net: Subthreads Stance–Rumor Interaction Network for rumor verification

Abstract

As online rumors have the potential to greatly affect areas such as social order, stock prices, and presidential elections, there is an emerging necessity for the automation of rumor verification. Although the current methods have achieved satisfactory performance, they still suffer from the following problems. First, the current methods simply concatenate the representations of different subthreads in their models, which may result in omitting some important information. Second, although stance information has been considered for the rumor verification task, it has not been fully utilized. To solve the problems, we propose the Subthreads Stance–Rumor Interaction Network (SSRI-Net) model for rumor verification. The proposed SSRI-Net model first introduces the Subthreads Interaction Attention mechanism between different subthreads to capture the interaction information between subthreads for a better understanding on user posts. Moreover, we also design the Stance–Rumor Interaction Network to fully integrate users’ stance information with rumor verification. We have conducted experiments on two public datasets, namely SemEval-2017 and PHEME datasets, for performance evaluation. Our SSRI-Net model outperforms the previous best models by 5.8% and 7.1% in Macro-F1 and Accuracy respectively on the SemEval-2017 dataset. In addition, our SSRI-Net model also outperforms the previous best models by 4.7% and 5.4% in Macro-F1 and Accuracy respectively on the PHEME dataset. The experimental results have shown that our proposed SSRI-Net model has outperformed the baseline models and achieved the state-of-the-art performance for rumor verification.