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

We study how strangers become friends within an evolving online social network. By modeling the co-evolution of individual users' friendship tie formations (when and with whom) and their concurrent online activities, we uncover important drivers underlying individuals' friendship decisions and, at the same time, quantify the resulting peer effects on individuals' actions. We estimate our model using a novel data set capturing the continuous development of a network and users' entire action histories within the network. Our results reveal that similarity (homophily) with a potential friend, the properties of a potential friend's network, and her domain expertise all play a role in friendship formation. Via prediction exercises, we find that stimulating anime watching is the most effective site-wide 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, our 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.