Rumor detection in social media based on eahanced Transformer - Honghao ZHENG; Yinuo HAO; Hongtao YU; Shaomei LI; Yiteng WU (网络与信息安全学报)
With the rapid development of the Internet, social media is increasingly integrated into all aspects of people’s daily life.Social media has gradually become a tool and even a platform for people to share opinions, insights, experiences and viewpoints.It is the main method for people to obtain and share information as well as express and exchange opinions.Currently, social media mainly includes social networking sites, Weibo, Twitter, blogs, forums, podcasts and so on.Due to the openness of social media, the user scale is large and the sources are complex and numerous, then all kinds of rumors and false information may be generated easily.Rumors on social media influence netizens’ understanding of events and shake the stability of society.Therefore, how to accurately and efficiently detect rumors has become an urgent problem to be solved.Existing Transformer based social media rumor detection models ignored the text location information.To effectively extract text location information and make full use of text potential information, a rumor detection model in social media was proposed and it was based on the enhanced Transformer.This model enhanced the traditional Transformer from two aspects of relative position and absolute position.It captured the direction information and distance information of the text using learnable relative position coding and mapped words from different positions to different feature spaces using absolute position coding.Experimental results show that, compared with the best benchmark model, the accuracy of the proposed model on Twitter15, Twitter16 and Weibo datasets is enhanced by 0.9%, 0.6% and 1.4%, respectively.Experimental results verify the effectiveness of the proposed location coding.And the enhanced Transformer based on location coding can significantly improve the effects of social media rumor detection.