Abstract
•Propose a rumor detection model based on entity and sentence hybrid model.•Design the sentence reconfiguration strategy based on semantic dependencies ranking.•Discuss the long-interval prediction and temporal effectiveness of the model.
Social media has recently become one of the most used media in the world. This has resulted in a great hotbed for the growth of rumors, as anyone can spread knowledge and opinions without confirmation. Previous works on rumor detection focused on hand-extracted features and spent less effort on text representation. In this research, a novel method for rumor detection on social media is proposed, which integrates entity recognition, sentence reconfiguration and ordinary differential equation network under a unified framework called ESODE. An entity recognition method to enhance the semantic understanding of rumor texts is used. Then, a sentence reconfiguration to improve the frequency of important words is designed. The complete feature map is established by further collecting statistical features from three aspects: linguistic features on the content of rumors, characteristics of users involved in rumor propagating, and propagation network structures. Finally, the ordinary differential equation network (ODEnet) is applied to detect rumors. Experimental results on datasets from Twitter and Weibo show that the proposed method achieves better performance than previous ones.