From Strangers to Friends: Tie Formations and Online Activities in an Evolving Social Network

Abstract

The authors study how strangers become friends within an evolving online social network. By modeling the coevolution of individual users’ friendship tie formations (when and with whom) and their concurrent online activities, the authors uncover important drivers underlying individuals’ friendship decisions and, at the same time, quantify the resulting peer effects on individuals’ actions. They estimate their model using a novel data set capturing the continuous development of a network and users’ entire action histories within the network. The results reveal that similarity (homophily) with a potential friend, the properties of a potential friend's network, and the potential friend’s domain expertise all play a role in friendship formation. Via prediction exercises, the authors find that stimulating anime watching is the most effective sitewide intervention, which leads to the highest overall site traffic and the largest number of active users, and that recommending a friend of a friend as a potential friend is the most effective strategy in stimulating friendship tie formation. In contrast to the common finding for static networks, the results indicate that seeding to users with the most friends is not the most effective strategy to increase users’ activity levels in an evolving network.