@inproceedings{97e7cf1016eb4d549221c0502921348f,
title = "Gender identification on social media",
abstract = "Accurate identification of hidden demographic attributes from social media is very useful for advertisement, personalized recommendation and etc. We investigate the effect of two different classification models for the gender identification problem over different attributes of Sina Weibo users. To improve the accuracy of the classfication models, we propose a novel feature selection algorithm and a retrained multiattribute model. Experimental results show that the accuracy of our approach achieves 89.01\% which is better than any previous work in this problem.",
keywords = "Gender identification, Retrained multi-attribute, Social media",
author = "Xiaofei Sun and Xiao Ding and Ting Liu",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 3rd National Conference on Social Media Processing, SMP 2014 ; Conference date: 01-11-2014 Through 02-11-2014",
year = "2014",
doi = "10.1007/978-3-662-45558-6",
language = "英语",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "99--107",
editor = "Jie Tang and Ting Liu and Heyan Huang and Hua-Ping Zhang",
booktitle = "Social Media Processing - 3rd National Conference, SMP 2014, Proceedings",
address = "德国",
}