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Kristen LeFevre

Kristen LeFevre

Kristen LeFevre joined Google as a Research Scientist in 2011. Her research interests include data management, data mining, privacy, and social systems. Prior to joining Google, she was an Assistant Professor at the University of Michigan. She received a Ph.D. in Computer Science from the University of Wisconsin-Madison and a Bachelor's Degree from Dartmouth College.

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    Look Who I Found: Understanding the Effects of Sharing Curated Friend Groups
    Lujun Fang
    Alex Fabrikant
    Kristen LeFevre
    Proceedings of ACM Web Science 2012, ACM, pp. 137-146
    Preview abstract Online 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+ recently launched 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 that circle-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 accelerates the 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 that can 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. View details
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