Uncovering the mystery of trust in an online social network

Trust is a hidden fabric of online social networks (OSNs) that enables online interactions, e.g., online transactions on Ebay. The fundamental properties of trust in OSNs, however, have not been adequately studied yet. In this work, we advance the understanding of trust in OSNs by analyzing the Advogato dataset [1]. We study the properties of direct trust, indirect trust, and trust community detection in Advogato. We found that 1) the trust between users are asymmetric, 2) high degree users are usually associated with high trust, 3) diversity in people´s opinions on the same person will affect indirect trust inference, 4) users live in many separate “small small worlds” from the perspective of trust and it is difficult to identify these “small small worlds” with existing random walk-based community detection algorithms, e.g., ACL [2]. It in fact motivates the need for a new community detection algorithm to identify clusters of user connected by trustful relations. Although our findings are from a specific OSN, they can significantly impact how OSNs are designed and configured in the future, e.g., a better user crowdsourcing setting based on trust information.