Rumor Detection Model Based on Improved Position Embedding

Abstract

With the rise of online social networks,the way people disseminate and obtain information has changed drastically.While social media facilitates people’s lives,it also accelerates the generation and spread of rumors.For this reason,detect rumors accurately and efficiently becomes an urgent problem to be solved.In order to improve the accuracy of rumor detection,the rumor detection model based on the global-local attention network has been improved.Taking into account the influence of the positional relationship between words in the text on rumor detection,a new relative position encoding method is introduced to improve the local feature extraction module of the original model.This method can more accurately extract and aggregate the semantic information and location information of the text in the rumor,and obtain better text features that distinguish between rumors and non-rumors.The combination of features and global features describing forwarding behavior improves the detection effect of rumors.Experimental results show that,compared with other mainstream detection methods,the F1 value of the proposed method can reach 95.0% on the Microblog data set,which has a better detection effect.

Keywords

• rumor detection|deep learning|attention mechanism|relative position embedding|rumor text features