[Objective] This paper tries to predict the number of retweets of government microblogs, aiming to evaluate the important features affecting retweets and public opinions.[Methods] First, we used the Convolutional Neural Network (CNN) and Gradient Boosting Decision Tree (GBDT) to combine user, time and content features. Then, we predicted the retweet numbers of government microblogs. Finally, we ranked the importance of every feature to find the most important one for retweets.[Results] The proposed model improved the accuracy of retweet prediction to 0.933. The semantic feature of microblog texts is the most important one.[Limitations] We did not study the impacts of indirect retweeting behaviors.[Conclusions] The CNN-GBDT model for deep-combined features could effectively predict retweets of government microblogs.
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