Exploring country level gender differences in the context of online dating using classification trees

Abstract

The key component of Social Networking Sites (SNS), gaining increasing popularity among Internet users, is the user profile, which plays a role of a self-advertisement in the aggregated form. While computer scientists investigate privacy implications of information disclosure, social scientists test or generate social or behavioral hypotheses based on the information provided by users in their profiles. Statistical analysis of the SNS phenomenon often is performed using only a very small sample of information extracted from a particular SNS or by interviewing students from a particular university. In this paper, we apply classification algorithm to a large-scale SNS dataset obtained from more than 10 million public profiles with 50 different attributes extracted from one of the largest dating sites in the Russian segment of the Internet. In particular we build gender classification models for the residents of the most active countries, and investigate the particular differences between genders in one country and the differences between the same-genders in different countries. The preliminary results are reported in this paper. To the best of our knowledge, this is the first attempt to conduct a large-scale analysis of SNS profiles and compare gender differences on a country level.