A syntactic multi-level interaction network for rumor detection

Online rumors could have a great impact on public order, stock prices and even the presidential election. Therefore, the detection of online rumors is imperative. Despite the satisfactory performance achieved by the current methods, there are still some issues that need to be addressed. First, most of the current methods have not taken into account imposing attentional constraints on important related words in the sentences, resulting in inaccurate attention being paid to some irrelevant words. Second, most of the current methods for detecting rumors fail to effectively incorporate contextual information from words or sentences. In this paper, we propose a syntactic multi-level interaction network model which incorporates syntactic dependency relationships and multi-level interaction network for rumor detection. First, the SMNet model uses a syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic dependency relationships into the attention mechanism for language-driven word representation. Then, the multi-level interaction network is applied to obtain a richer semantic representation. After that, the global relation encoding capture the rich structural information and the rumor classification is performed to generate the verification result. We have conducted experiments on Weibo, Twitter15 and Twitter16 datasets for performance evaluation. Our SMNet model has achieved an accuracy of 95.9% on the Weibo dataset. In addition, our SMNet model has achieved an accuracy of 91.7% and 93.5% on Twitter 15 and Twitter 16, respectively. The experimental results show that our proposed SMNet model outperforms the baseline models and achieves the state-of-the-art performance for rumor detection.