Abstract: In the fast-developing Internet era,Weibo brings a large amount of information,but there exists water army in Weibo topic.To a certain extent,the water army affects ordinary users to understand the real situation.In order to efficiently and accurately identify the water army,the semi-supervised collaborative training algorithm is considered comprehensively in view of the small number of water military samples and the large number of non-water military samples.By studying and analyzing multiple characteristics of Weibousers,the proposed algorithm redefines six attribute feature values,such as account attention,daily microblog number,and microblog influence.According to the characteristics of the algorithm,the six attribute feature values are divided into two attribute sets,each attribute set corresponds to one view,and each view uses seven classification methods in the Scikit-Learn machine learning library to train the classifier to identify the water army.Finally,experiments are conducted on dataset.The results show that the accuracy,recall rate,accuracy and F1-measure value of the classification results are higher when the two views use the naive Bayes algorithm and the logistic regression algorithm to train the classifier.Therefore,comprehensive analysis of Weibo user cha-racteristics and the use of semi-supervised collaborative training algorithms in line with the actual situation can accurately,efficiently and quickly identify Weibo water army.

Key words: Classifier, Collaborative training, Semi-supervised, Water army identification