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

机译:Facebook告诉我你的性别:对土耳其Facebook用户的性别预测进行了探索性研究

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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.
机译:在线社交网络(OSN)是社交互动的非常流行的平台。公开发布的奥斯人的数据对奥斯用户的个人隐私构成了各种威胁。对手可以尝试预测私有属性值,例如性别,以及链接/连接。量化侵犯福利人推断奥斯用户的性别的能力是迈向隐私保护的重要第一步。对预测作者/用户的性别的问题,特别是在英语语言的背景下进行了许多研究。相反,这场领域的研究对于土耳其语而言非常有限,特别是在OSN的领域中。以前对土耳其OSN用户的性别预测的研究主要是通过使用推文和Facebook评论的内容来执行的。在本文中,我们建议使用Facebook OSN的性别预测问题的各种功能,而不仅仅是用户评论。与现有研究不同,我们利用了从简介,墙面内容和网络结构提取的功能,以及用户的墙壁交互。因此,我们的研究与现有的工作不同于所考虑的功能的宽广,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,基本档案信息提供了更好的结果;此外,使用该信息与墙壁交互一起提高预测质量。我们测量了0.982的最佳精度值,通过组合土耳其OSN用户的简档数据和墙壁交互来获得。在墙壁交互模型中,我们介绍了34种不同的特征,提供比土耳其语的现有内容的研究结果更好。

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