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 and their concurrent online activities (product adoptions and content generation), we are able to discover important drivers underlying individuals' friendship decisions and, at the same time, to 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 knowledgeability of and similarity (homophily) with a potential friend are the most important drivers of friendship formation. Next, through prediction exercises, we assess how to most effectively increase website traffic in an evolving network via seeding and stimulation strategies. And lastly, in contrast to results for static networks, we find that seeding to users with the most friends is not the most effective strategy to increase users' activity levels in an evolving network