Online 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.

中文翻译：

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Facebook 告诉我你的性别：土耳其 Facebook 用户性别预测的探索性研究

在线社交网络 (OSN) 是非常流行的社交互动平台。通过 OSN 公开发布的数据对 OSN 用户的个人隐私构成各种威胁。攻击者可以尝试预测私有属性值，例如性别以及链接/连接。量化对手推断 OSN 用户性别的能力是隐私保护的重要第一步。已经对预测作者/用户的性别问题进行了大量研究，特别是在英语语境中。相反，该领域的研究对于土耳其语，特别是在 OSN 领域非常有限。以前对土耳其 OSN 用户性别预测的研究大多是通过使用推文和 Facebook 评论的内容来进行的。在本文中，我们建议使用各种功能，而不仅仅是用户评论，来解决 Facebook OSN 上的性别预测问题。与现有研究不同，我们利用了从个人资料、墙内容和网络结构以及用户的墙交互中提取的特征。因此，我们的研究与现有工作的不同之处在于所考虑的特征的广泛性、应用的机器学习和深度学习方法，以及实验评估中使用的 OSN 数据集的大小。我们的结果表明，基本的个人资料信息提供了更好的结果；此外，将这些信息与墙壁相互作用一起使用可以提高预测质量。我们测得的最佳准确度值为 0.982，这是通过结合土耳其 OSN 用户的配置文件数据和墙交互获得的。在墙相互作用模型中，