Understanding Cross-site Linking in Online Social Networks

Abstract

Online social networks (OSNs) have attracted billions of users, and play an important role in people's daily life. A user often has accounts on multiple OSN sites. In this paper, we study the emerging "cross-site linking" function, which is supported by many OSNs. Our study is based on Foursquare, a representative location-based social networking (LBSN) service. We conduct a data-driven analysis by using crawled public profiles of almost all (if not all) Foursquare users. Our analysis has shown that the crosssite linking function is widely adopted by Foursquare users, and the users who have enabled this function are more active than other users. We have also found that users who are more concerned with online privacy have a lower probability to enable the cross-site linking function. Besides analyzing crawled Foursquare user profiles, we further explore cross-site linking between Foursquare and other OSN sites, i.e., Facebook and Twitter. The study on "FoursquareFacebook" linking indicates that users have a high probability to provide consistent information to different OSNs. Meanwhile, "Foursquare-Twitter" linking is used to demonstrate the usefulness of aggregating user-generated content across multiple OSN sites. We present a gender-based analysis of Twitter, which is made accurate by leveraging crosssite links between Foursquare and Twitter.

Harvesting Multiple Sources for User Profile Learning

“…The authors observed that active Foursquare users are usually active in Twitter as well, which is consistent with the observations derived from our dataset. In other study, Chen et al [4] tried to discover cross-site linking regularities in largescale online social networks dataset. By gathering almost all Foursquare user network graph and applying first order statistical analysis, the authors [4] were able to extract comprehensive knowledge about the Foursquare network structure and users cross social network behaviour.…”

“…In other study, Chen et al [4] tried to discover cross-site linking regularities in largescale online social networks dataset. By gathering almost all Foursquare user network graph and applying first order statistical analysis, the authors [4] were able to extract comprehensive knowledge about the Foursquare network structure and users cross social network behaviour. At the same time, Zhong et al [29] attempted to discover network evolution relations across various social forums datasets (Tencent, Epinion, Facebook, Xiaonei, Twitter, Sina Weibo, Github, StackOverflow).…”

“…Furthermore, it is well known that more than half of online adults use more than one social network in their daily life 1 . These social services are often connected to each other by deploying the so-called cross-linking functionality [4]. With multi-source and mobility-related data, understanding user behaviours via user profile learning is promising and possible [13] [15][17] [25] [26].…”

#mytweet via Instagram

“…This is often a mandatory step for users to perform cross-posting. Chen et al, using a data-driven approach, investigated how users cross-linked their Foursquare accounts to other OSNs, namely Facebook and Twitter [14]. Most relevant to cross-posting is work by Ottoni et al [15].…”

Understanding Service Integration of Online Social Networks: A Data-Driven Study

“…Numerous online social networks (OSNs) have emerged in recent years. Many of them, such as Foursquare, Pinterest, Quora and Medium, have enabled the cross-site linking function [1]. This function allows a user to link her account on one OSN to her accounts on other OSNs.…”