Gender Identification Through Facebook Data Analysis Using Machine Learning Techniques

被引：3

作者：

Kiratsa, P. I. ^{[1
]}

Sidiropoulos, G. K. ^{[1
]}

Badeka, E. V. ^{[1
]}

Papadopoulou, C. I. ^{[1
]}

Nikolaou, A. P. ^{[1
]}

Papakostas, G. A. ^{[1
]}

机构：

[1] Eastern Macedonia & Thrace Inst Technol, HUMAIN Lab, Dept Comp & Informat, Kavala, Greece

来源：

22ND PAN-HELLENIC CONFERENCE ON INFORMATICS (PCI 2018) | 2018年

关键词：

gender identification; facebook profile; machine learning methods; data mining techniques; social networks;

D O I：

10.1145/3291533.3291591

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The purpose of this paper is to analyze Facebook users' profile aiming at identifying the gender of the profile's owner. To this end several machine learning models were adopted and applied on a representative set of features extracted from Facebook profiles describing users' preferences relative to their gender information. This study concludes that there is a plethora of features which can be mined from a Facebook profile and can be used in identifying the gender of a profile's owner. Moreover, the experiments reveal that this gender identification task can be accomplished effectively by using machine learning techniques with 97.30% accuracy, after considering a large amount of Facebook profile data.

引用

页码：117 / 120

页数：4

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