Abstract: Aiming to solve the issue that the accuracy of micro-blog retweet behavior prediction is not good enough and features are selected with an arbitrary choice,a new method using anomaly detection and random forest algorithms to predict micro-blog retweet behavior was proposed.Firstly,the basic features of the user,the basic characteristics of blog and blog content theme features are extracted,and the user activity and blog influence are calculated based on relative entropy.Secondly,the best feature set are selected by combining the filter and wrapper feature selection method.Finally,anomaly detection and random forest algorithms are fused to predict micro-blog retweet behavior based on selected features.The algorithm parameters of random forest are selected by analyzing the error estimation of out of bag data.By contrasting with Logistic Regression,Decision Tree,Naive Bias and Random Forest algorithms,which are used in the analysis for micro-blog retweet behavior,the prediction accuracy of the proposed method is higher than that of the optimal random forest method on real data,and reaches 90.5%.Meanwhile,the validity of feature selection method is verified.

Key words: Retweet prediction,Random forest,Anomaly detection,Feature filter,Relative entropy