Reading the Source Code of Social Ties

TL;DR

This paper introduces an unsupervised, parameter-free method to identify fundamental interaction domains in online social networks, revealing status, knowledge, and support exchanges, and linking these to social network analysis concepts.

Contribution

It proposes a novel unsupervised approach to discover interaction domains in social networks, bridging the gap between observed data and social theory.

Findings

Three interaction domains: status, knowledge, social support.

Significant relations between domains and social network metrics.

Method applicable to various online social media analysis tasks.

Abstract

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 relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe 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 induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.