EDRN-based propagation model for popular microblog information detection

Abstract

Microblogging platforms have become popular for sharing knowledge and communicating with others. Rumours and current trends can be identified promptly if popular information trends on these platforms can be detected in the early stages of dissemination. Microblog information propagation is primarily based on forwarders; the forwarded information is more likely to become popular if the forwarder's influence is greater and the time interval between two adjacent forwarders is shorter. Based on the explicit duration recurrent network (EDRN), a recurrent neural network, a new microblog information-propagation model for the early detection of popular information was presented. In this model, the observation is constructed based on the forwarder's influence level and the time interval between two adjacent forwarders. The influence level of the forwarder was estimated using the support vector machine classifier. To evaluate this model, experiments were conducted using Twitter and Weibo datasets. In the experiments, EDRN outperformed several popular recurrent neural networks, hidden semi-Markov models, and hidden Markov models, particularly when the training set was small. The timeliness evaluation results demonstrated that the model can effectively detect popular information at an early stage.