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.
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Sun, X., Ding, X., Liu, T. (2014). Gender Identification on Social Media. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_9
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DOI: https://doi.org/10.1007/978-3-662-45558-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
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