Gender identification through facebook data analysis using machine learning techniques
Polixeni Kiratsa, George K. Sidiropoulos, Eftichia Badeka, Cristina I. Papadopoulou, A. P. Nikolaou, George A. Papakostas · 2018
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.