Rumor Detection Based on Representative User Characteristics Learning Through Propagation

Effective rumor detection and management has become an essential part of Internet plus government services initiative. The Internet era brings great convenience to people’s communication as well as speeds up the propagation of rumors, which not only interferes people’s normal living but also does harm to the social confidence system. Existing work of rumor debunking on the Internet is mostly based on manual work of public tip-offs and screening, which is time consuming and demanding. Meanwhile, work on algorithm of rumor detection based on data mining and machine learning depends heavily on text content, which is deficient during the early stage of rumor propagation. This paper constructs latest dataset Weibo2020, composed of both rumors and normal information, and extracts representative user characteristics from the perspective of statistics, then proposes an algorithm of early-stage rumor detection based on brief propagation path, named RPPC (representative propagation path classification). The experimental results indicate that the proposed method can improve the prediction accuracy by 2.57 percentage points while reducing the input data scale by 50%. Meanwhile, the proposed method can predict the authenticity of news released in 5 minutes and achieve an accuracy of about 80%. Therefore, the proposed method achieves good results in a limited size of dataset and can to some degree help with network public opinion governance and improve the efficiency and quality of government service.