Círculos Google

Look Who I Found: Understanding the Effects of Sharing Curated Friend Groups
Lujun Fang University of Michigan 2260 Hayward Avenue Ann Arbor, MI 48109 ljfang@umich.edu Alex Fabrikant Google Research 1600 Amphitheatre Pkwy Mountain View, CA 94043 fabrikant@google.com Kristen LeFevre Google Research 1600 Amphitheatre Pkwy Mountain View, CA 94043 klefevre@google.com

ABSTRACT

General TermsOnline social networks like Google+, Twitter, and Facebook allow users to build, organize, and manage their social connections for the purposes of information sharing and consumption. Nonetheless, most social network users still report that building and curating contact groups is a time consuming burden. To help users overcome the burdens of contact discovery and grouping, Google+ recentlylaunched a new feature known as “circle sharing.” The feature makes it easy for users to share the benefits of their own contact curation by sharing entire “circles” (contact groups) with others. Recipients of a shared circle can adopt the circle as a whole, merge the circle into one of their own circles, or select specific members of the circle to add. In this paper, we investigate the impact thatcircle-sharing has had on the growth and structure of the Google+ social network. Using a cluster analysis, we identify two natural categories of shared circles, which represent two qualitatively different use cases: circles comprised primarily of celebrities (celebrity circles), and circles comprised of members of a community (community circles). We observe that exposure to circle-sharing acceleratesthe rate at which a user adds others to his or her circles. More specifically, we notice that circle-sharing has accelerated the “densification” rate of community circles, and also that it has disproportionately affected users with few connections, allowing them to find new contacts at a faster rate than would be expected based on accepted models of network growth. Finally, we identify features thatcan be used to predict which of a user’s circles (s)he is most likely to share, thus demonstrating that it is feasible to suggest to a user which circles to share with friends.
Author Keywords

Experimentation.
INTRODUCTION

Every day, hundreds of millions of users enjoy sharing and consuming information using online social network sites. At the same time, it can be difficult for users todiscover new contacts and to maintain contact groupings (e.g., Google+ circles, Facebook friend lists)[21, 7]. Most contact management solutions focus on only one of these two tasks. A significant amount of research focuses on link prediction, which can be used to recommend new contacts to social network users [17, 14]. These recommendations are often made based on the user’s existing connections, whichmeans that they are less accurate for new users (the “cold-start” problem). Moreover, link prediction algorithms usually generate one recommendation at a time. On the other hand, contact grouping is notoriously difficult for users [21]. A number of data mining and machine learning approaches have been proposed and built to automatically group contacts [1, 4, 10], but none of them generatessatisfactory user groups without user involvement. Further, existing tools typically cannot detect real-life communities until many of the community’s interconnections are already captured in the online system [13]. As a result, new users and users of nascent social networks are often forced to manually curate and populate lists to capture the natural groupings among their contacts. In September 2011,Google+ launched a “circle-sharing” tool, which allows users to share their individual circles (i.e., contact groups) with other users [3]. A screenshot of the circlesharing tool is shown in Figure 1. Recipients of a shared circle can copy the circle as-is, merge the circle into one of their existing circles, or cherry-pick people from the circle to add to their own circles. In this paper, we...