Weaving Space into the Web of Trust: An Asymmetric Spatial Trust Model for Social Networks

The proliferation of Geo-Information (GI) production in web-based collaboration environments such as mapping mashups built on top of mapping APIs such as GoogleMaps API poses new challenges to GI Science. In this environment, millions of users are not only consumers of GI but they are also producers. A major challenge is how to manage this huge flow of information and identify high value contributions while discarding others. The social nature of the collaborative approaches to GI provides the inspiration for innovative solutions. In this paper, we propose a novel spatial trust model for social networks. This model is part of our research to formalize the spatio-temporal regularities of trust in social networks. The presented model provides a metric for trust as a proxy for GI quality to assess the value of collaborative contributions. We also introduce the underlying network for the model, which is a hybrid network structure for collaborative GI applications based on affiliation networks and one-mood continuous trust networks. Trust calculation in the affiliation network takes into account the geographic distance between the actors and their information contributions –also known as events–, while the one-mood network does not. This leads to an asymmetric model with respect to the representation of space.