Exploring gender differences in member profiles of an online dating site across 35 countries

Slava Kisilevich, Mark Last

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Online communities such as forums, general purpose social networking and dating sites, have rapidly become one of the important data sources for analysis of human behavior fostering research in different scientific domains such as computer science, psychology, anthropology, and social science. The key component of most of the online communities and Social Networking Sites (SNS) in particular, is the user profile, which plays a role of a self-advertisement in the aggregated form. While some scientists investigate privacy implications of information disclosure, others test or generate social and behavioral hypotheses based on the information provided by users in their profiles or by interviewing members of these SNS. In this paper, we apply a number of analytical procedures on a large-scale SNS dataset of 10 million public profiles with more than 40 different attributes from one of the largest dating sites in the Russian segment of the Internet to explore similarities and differences in patterns of self-disclosure. Particularly, we build gender classification models for the residents of the 35 most active countries, and investigate differences between genders within and across countries. The results show that while Russian language and culture are unifying factors for people's interaction on the dating site, the patterns of self-disclosure are different across countries. Some geographically close countries exhibit higher similarity between patterns of self-disclosure which was also confirmed by studies on cross-cultural differences and personality traits. To the best of our knowledge, this is the first attempt to conduct a large-scale analysis of SNS profiles, emphasize gender differences on a country level, investigate patterns of self-disclosure and to provide exact rules that characterize genders within and across countries.

Original languageEnglish
Title of host publicationAnalysis of Social Media and Ubiquitous Data - International Workshops, MUSE 2010, Revised Selected Papers
Pages57-78
Number of pages22
DOIs
StatePublished - 20 Sep 2011
EventInternational Workshop on Mining Ubiquitous and Social Environments, MUSE 2010, in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2010 - Barcelona, Spain
Duration: 20 Sep 201020 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6904 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Mining Ubiquitous and Social Environments, MUSE 2010, in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2010
Country/TerritorySpain
CityBarcelona
Period20/09/1020/09/10

Keywords

  • Classification trees
  • Gender differences
  • Hierarchical Clustering
  • Multidimensional Scaling
  • Self-disclosure
  • Social Networking Sites

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