Social Networking by Proxy: A Case Study of Catster, Dogster and Hamsterster

TL;DR

This paper studies multi-profile online pet social networks to understand the relationship between pet and human networks, and introduces methods to predict if profiles belong to the same user with high accuracy.

Contribution

It introduces the concept of multi-profile social networks, extends spectral analysis, and develops algorithms for user-profile association prediction.

Findings

High-precision prediction of profiles created by the same user

Analysis of pet-level and human-level network interactions

Development of new homophily measures for multi-profile networks

Abstract

The proliferation of online social networks in the last decade has not stopped short of pets, and many different online platforms now exist catering to owners of various pets such as cats and dogs. These online pet social networks provide a unique opportunity to study an online social network in which a single user manages multiple user profiles, i.e. one for each pet they own. These types of multi-profile networks allow us to investigate two questions: (1) What is the relationship between the pet-level and human-level network, and (2) what is the relationship between friendship links and family ties? Concretely, we study the online social pet networks Catster, Dogster and Hamsterster, the first two of which are the two largest online pet networks in existence. We show how the networks on the two levels interact, and perform experiments to find out whether knowledge about friendships on a profile-level alone can be used to predict which users are behind which profile. In order to do so, we introduce the concept of multi-profile social network, extend a previously defined spectral test of diagonality to multi-profile networks, define two new homophily measures for multi-profile social networks, perform a two-level social network analysis, and present an algorithm for predicting whether two profiles were created by the same user. As a result, we are able to predict with very high precision whether two profiles were created by a same user. Our work is thus relevant for the analysis of other online communities in which users may use multiple profiles.