Weibo popularity prediction attempts to forecast the future diffusion range of Weibo messages based on the propagation features at early stages. The existing methods are mainly depended on messages’ early popularity, ignoring the propagation trend at that time, which leads to poor predicting accuracy when these methods applied on Weibo messages. For the purpose of forecasting the Weibo popularity more accurately and conveniently, we propose a multiple linear regression model: UAPA (user activity propagation acceleration) in this paper. Firstly, we investigate the relationship between future popularity and varying trend of Weibo diffusion, and find that they are positive correlation. Based on this detection, we present the concept of propagation acceleration which describes the spreading varying speed of Weibo, then we build a predicting model based on propagation acceleration and popularity at early stages. Furthermore, we analyze the Weibo user periodic activity and find that the retweeting times of users vary greatly at different time in one day, and the messages’ popularity and propagation acceleration are also distinct at various moments. In the light of this finding, we optimize the predicting model by user activity. Finally, we compare prediction accuracy of UAPA model mentioned above with representative popularity prediction methods on two real datasets, with 1000 thousands and 410 thousands messages respectively, and discuss the influence of parameter value in UAPA model on prediction performance. Experiments show that UAPA model is superior to the existing methods on multiple indicators.