Exploration of Robust Features of Trust Across Multiple Social Networks

In this paper, we investigate the problem of trust formation in virtual world interaction networks. The problem is formulated as one of link prediction, intranet work and internet work, in social networks. We use two datasets to study the problem - SOE's Ever quest II MMO game dataset and IBM's Small Blue sentiments dataset. We explore features based on the node's individual properties as well as based on the node's location within the network. In addition, we take into account the node's participation in other social networks within a specific prediction task. Different machine learning models built on the features are evaluated with the goal of finding a common set of features which are both robust and discriminating across the two datasets. Shortest Distance and Sum of Degree are found to be robust, discriminating features across the two datasets. Finally, based on experiment results and observations, we provide insights into the underlying online social processes. These insights can be extended to models for online social trust.