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Gender identification through facebook data analysis using machine learning techniques

Authors:

G. A. PapakostasAuthors Info & Claims

PCI '18: Proceedings of the 22nd Pan-Hellenic Conference on Informatics

Pages 117 - 120

https://doi.org/10.1145/3291533.3291591

Published: 29 November 2018 Publication History

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Abstract

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.

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Ho Huong TTran-Trung KTruong Hoang V(2022)A Computational Linguistic Approach for Gender Prediction Based on Vietnamese NamesMobile Information Systems10.1155/2022/65702282022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6570228
Silva DMaziero ESaadi MRosa RSilva JRodriguez DIgorevich K(2022)Big data analytics for critical information classification in online social networks using classifier chainsPeer-to-Peer Networking and Applications10.1007/s12083-021-01269-1Online publication date: 10-Jan-2022
https://doi.org/10.1007/s12083-021-01269-1

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Information & Contributors

Information

Published In

PCI '18: Proceedings of the 22nd Pan-Hellenic Conference on Informatics

November 2018

336 pages

ISBN:9781450366106

DOI:10.1145/3291533

Editors:
Karanikolas Nikitas
University of West Attica
,
Mamalis Basilis
University of West Attica
,
General Chairs:
Kontos John
University of Athens
,
Pantziou Grammati
University of West Attica
,
Gritzalis Stefanos
University of the Aegean
,
Douligeris Christos
University of Piraeus

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 November 2018

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PCI '18

PCI '18: 22nd Pan-Hellenic Conference on Informatics

November 29 - December 1, 2018

Athens, Greece

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PCI '18 Paper Acceptance Rate 57 of 105 submissions, 54%;

Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

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Ho Huong TTran-Trung KTruong Hoang V(2022)A Computational Linguistic Approach for Gender Prediction Based on Vietnamese NamesMobile Information Systems10.1155/2022/65702282022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6570228
Silva DMaziero ESaadi MRosa RSilva JRodriguez DIgorevich K(2022)Big data analytics for critical information classification in online social networks using classifier chainsPeer-to-Peer Networking and Applications10.1007/s12083-021-01269-1Online publication date: 10-Jan-2022
https://doi.org/10.1007/s12083-021-01269-1

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