In view of the fact that the propagation path topology cannot effectively deal with complex social network consists of hundreds of millions of users. More researchers choose to use machine learning methods to complete retweet prediction. Those use the classification method to judge whether a message will be retweeted or not. This paper argues that retweet prediction should be regression analysis problem, not just the classification problem. Through collecting user characteristics on Twitter and

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... selecting some features which have an important impact on the retweet behavior, a Prediction algorithm Based on the Logistic Regression for users Retweet Times in social network was proposed. Experiment results based on the actual data set show the regression analysis predicting model has a good predicting accuracy in dealing with retweet predicting, the proposed method is effectiveness. This paper reposts on building regression analysis model based on Logistic Regression algorithm to predicting the scale of information dissemination. We selected some features that have an important impact on the retweet behavior and divided into four categories, including user features, text features, temporal feature and metadata feature. To note is that we take into account the effect of text content of the retweet behavior.