Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (2): 349-356.doi: 10.23940/ijpe.18.02.p16.349356

• Original articles • Previous Articles     Next Articles

Inferring Gender of Micro-Blog Users based on Multi-Classifiers Fusion

Jinghua Zhenga, *, Shize Guob, Liang Gaob, Di Xuec, Nan Zhaod, and Huimin Maa   

  1. aHefei electronic engineering institute, Hefei, 230037, China
    bInstitute of North Electronic Equipment, Beijing, 100083, China
    cArmy Engineering University, Nanjing, 210007, China
    dCAS Institute of Psychology, Beijing, 100101, China


Knowing user demographic traits offers a great potential for public information. Most research have used local features to predict user demographic traits. Since this method did not make the most of user global features, the prediction performance was low. In this paper, our goal tries to use an ensemble learning method to improve the prediction performance through multi-classifiers fusion. Our work makes three important contributions. Firstly, we show how to predict Sina Micro-blog users’ genders based on his/her text published on the social network. Secondly, we show that user’s personality traits can also be used to infer gender. And last and thirdly, we propose multi-classifiers fusion to predict users’ genders, and give the experimental results that validate our method by comparing it with a different local features dataset. Our experiment demonstrates that our method can improve the accuracy rate, the recall rate of prediction, and the F value.