The rise of social broadcasting technologies has greatly facilitated open access to information worldwide, not only by powering decentralized information production and consumption, but also by expediting information diffusion through social interactions like content sharing. Voluntary information sharing by users in the context of Twitter, the predominant social broadcasting site, is studied by modeling both the technology and user behavior. A detailed data set about the official content-sharing function on Twitter, called retweet, is collected and the statistical relationships between users’ social network characteristics and their retweeting acts are documented. A two-stage consumption-sharing model is then estimated using the conditional maximum likelihood estimatio (MLE) method. The empirical results convincingly support our hypothesis that weak ties (in the form of unidirectional links) are more likely to engage in the social exchange process of content sharing. Specifically, we find that after a median quality tweet (as defined in the sample) is consumed, the likelihood that a unidirectional follower will retweet is 3.1 percentage point higher than the likelihood that a bidirectional follower will do so.
In recent years, there has been stellar growth of location-based/enabled social networks in which people can "check in" to physical venues they are visiting and share with friends. In this paper, we hypothesize that the "check-ins" made by friends help users learn the potential payoff of visiting a venue. We argue that this learning-in-a-network process differs from the classic observational learning model in a subtle yet important way: Rather than from anonymous others, the agents learn from their network friends, about whose tastes in experience goods the agents are better informed. The empirical analyses are conducted on a unique data set in which we observe both the explicit interpersonal relationships and their ensuing check-ins. The key result is that the proportion of checked-in friends is not positively associated with the likelihood of a new visit, rejecting the prediction of the conventional observational learning model. Drawing on the literature in sociology and computer science, we show that weighting the friends' check-ins by a parsimonious proximity measure can yield a more intuitive result than the plain proportion does. Repeated check-ins by friends are found to have a pronounced effect. Our empirical result calls for the revisiting of observational learning in a social network setting. It also suggests that practitioners should incorporate network proximity when designing social recommendation products and conducting promotional campaigns in a social network.