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      Displaying Things in Common to Encourage Friendship Formation: A Large Randomized Field Experiment

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          Abstract

          Friendship formation is important to online social network sites and to society, but can suffer from informational friction. In this study, we demonstrate that social networks may effectively use an IT-facilitated intervention -- displaying things in common (TIC) between users (mutual hometown, interest, education, work, city) -- to encourage friendship formation. Displaying TIC updates an individual's belief about the shared similarity with another and reduces information friction that may be hard to overcome in offline communication. In collaboration with an online social network, we conduct a randomized field experiment that randomly varies the prominence of different things in common when a user is browsing a non-friend's profile. The dyad-level exogenous variation, orthogonal to any (un)observed structural factors in viewer-profile's network, allows us to cleanly isolate the role of preferences for TIC in driving network formation and homophily. We find that displaying TICs to viewers can significantly increase their probability of sending a friend requests and forming friendships, and is especially effective for pairs of people who have little in common. We also find that displaying TIC can improve friendship formation for a wide range of viewers with different demographics and is more effective when the TICs are more surprising to the viewer.

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          Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.

          Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300-700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.
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            Identifying influential and susceptible members of social networks.

            Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible to influence than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product offered. Analysis of influence and susceptibility together with network structure revealed that influential individuals are less susceptible to influence than noninfluential individuals and that they cluster in the network while susceptible individuals do not, which suggests that influential people with influential friends may be instrumental in the spread of this product in the network.
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              Opinion Leadership and Social Contagion in New Product Diffusion

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                Author and article information

                Journal
                07 May 2019
                Article
                1905.02762
                30cbecff-d2f5-42fe-8705-54b83ff2d34e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                20th ACM conference on Economics and Computation
                cs.SI

                Social & Information networks
                Social & Information networks

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