Reading the source code of social ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the re-lation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by propos-ing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in so-cial networks. By applying this method on two online datasets dif-ferent by scope and type of interaction (aNobii and Flickr) we ob-serve the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions in-duced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incor-porate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from rec-ommendation to visual link summarization