Web user's online interacting behavior with others usually makes some user generated content (e.g. forum threads and Weibo topics) popular. The modeling and prediction of the popularity of online content are of great research importance and practical value in many different domains. To predict the popularity of forum threads, this paper discusses several dynamic factors that might affect the popularity of online content based on the information of dynamic evolution at the early stage, and proposes a popularity prediction algorithm which makes use of the locality property and combines multiple dynamic factors. The proposed algorithm is further evaluated with the Douban group dataset. The experimental results show that, compared with the baseline methods, our method achieves relatively better performance in predicting the popularity of forum threads.