Social Discovery: Exploring the Correlation among Three-Dimensional Social Relationships

This paper explores the correlation among three kinds of social relationships: face-to-face social relationship, online social relationship, and self-report social relationship. An experiment was carried out to collect users' three-dimensional social data: real-world mobile trace data, virtual-world online social data, and self-report social data. By analyzing network structure, we find that friendship in online social networks can better describe self-report friendship compared to friendship created by frequent physical encounters. Several supervised classifiers with the combination of features extracted from mobile trace data and online social data are used to predict the self-report social relationship under different social strengths. Results show that the proposed model can correctly predict more than 80% friends under strongest social tie strength. What is more, we define social popularity according to social relationships self-reported by users. By comparing social popularity with online and offline social behaviors, we find diversity in weekend is a good measure to describe social popularity.