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Identifying Peer Influence in Massive Online Social Networks: A Platform for Randomized Experimentation on Facebook

Introduction Identifying peer to peer influence in social networks is a difficult and fundamental problem in numerous disciplines including network theory, marketing, epidemiology and diffusion research. It is widely believed that the cleanest way to examine the magnitude of peer effects and the social, structural and personal circumstances under which influence is more or less pronounced is to observe large scale, real world randomized trials of person to person communications intended to influence particular economic decisions. In this research we explore this approach to the identification of peer effects using random experiments in a massive online social network. Recent research demonstrates that individual economic behaviors (such as product adoption or work performance) tend to cluster in social networks, in both network space (assortative mixing) and in time (temporal clustering). Yet, while evidence of assortative mixing and temporal clustering of economic outcomes in networks may indicate peer to peer influence, these outcomes may also be explained by homophily-the demographic, technological, behavioral, and biological similarities of linked nodes (Jackson 2008). If ties are more likely between similar nodes, their outcomes could be correlated due to inherent similarities in their characteristics rather than as a consequence of their interactions. On one hand, linked nodes may directly influence one another to exhibit similar outcomes, creating viral contagions. On the other hand, linked nodes may simply have greater likelihoods of displaying correlated outcomes, in time and in network space, as a consequence of their similarities. Several sophisticated econometric techniques have been developed in the hopes of untangling these explanations. Work on the identification of peer effects in networks (e.g. Brock and Durlauf 2001; Bramoulle 2007) has developed following (Manski 1993) and (Frank and Strauss 1986), or models of the co-evolution of networks and behaviors by (Snijders 2006), dynamic matched sampling techniques (Aral et al. 2008), methods based on exogenous shocks to peers (Tucker 2008), or examination of natural experiments (e.g. Sacerdote 2001). However, each of these methods suffers from its own limitations: identification conditions are strict, methods are not typically scalable to large networks, observation of naturally occurring random assignment is rare, and shocks to peers used as instruments are rarely truly exogenous because social relationships typically signal unobserved reasons why these shocks should be correlated amongst peers. We look to randomized trials involving millions of users of an online social network to overcome some of these limitations. Over the last year we have built …