Facebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users

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dc.contributor.authorCoban, Onder
dc.contributor.authorInan, Ali
dc.contributor.authorOzel, Selma Ayse
dc.date.accessioned2025-01-06T17:37:48Z
dc.date.available2025-01-06T17:37:48Z
dc.date.issued2021
dc.description.abstractOnline Social Networks (OSNs) are very popular platforms for social interaction. Data posted publicly over OSNs pose various threats against the individual privacy of OSN users. Adversaries can try to predict private attribute values, such as gender, as well as links/connections. Quantifying an adversary's capacity in inferring the gender of an OSN user is an important first step towards privacy protection. Numerous studies have been made on the problem of predicting the gender of an author/user, especially in the context of the English language. Conversely, studies in this field are quite limited for the Turkish language and specifically in the domain of OSNs. Previous studies for gender prediction of Turkish OSN users have mostly been performed by using the content of tweets and Facebook comments. In this article, we propose using various features, not just user comments, for the gender prediction problem over the Facebook OSN. Unlike existing studies, we exploited features extracted from profile, wall content, and network structure, as well as wall interactions of the user. Therefore, our study differs from the existing work in the broadness of the features considered, machine learning and deep learning methods applied, and the size of the OSN dataset used in the experimental evaluation. Our results indicate that basic profile information provides better results; moreover, using this information together with wall interactions improves prediction quality. We measured the best accuracy value as 0.982, which was obtained by combining profile data and wall interactions of Turkish OSN users. In the wall interactions model, we introduced 34 different features that provide better results than the existing content-based studies for Turkish.
dc.identifier.doi10.1145/3448253
dc.identifier.issn2375-4699
dc.identifier.issn2375-4702
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85120314166
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1145/3448253
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2372
dc.identifier.volume20
dc.identifier.wosWOS:000721582900013
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAssoc Computing Machinery
dc.relation.ispartofAcm Transactions on Asian and Low-Resource Language Information Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFacebook
dc.subjectonline social networks
dc.subjectattribute inference
dc.subjectgender detection
dc.subjecttext categorization
dc.titleFacebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users
dc.typeArticle

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