In group decision making scenarios, where multiple anonymous agents interact, as is the case of social networks, the uncertainty in the provided information as well as the diversity in the experts' opinions make of them a real challenge from the point of view of information aggregation and consensus achievement. This contribution addresses these two main issues in the following way: On the one hand, in order to deal with highly uncertainty group decision making scenarios, whose main particularity is that some of their experts may not be able to provide any single judgment about an alternative, the proposed approach estimates these missing information using the preferences coming from other trusted similar experts who present high degrees of confidence and consistency. On the other hand, with the objective of increasing the consensus among the agents involved in the decision making process, a feedback based influence network has been proposed. In this network, the influence between the agents is calculated by means of a dynamic combination of the inter agents trust, their self confidence, and their similarity. Thanks to this influence network our approach is able to recognize and isolate malicious users adjusting their influence according to the trust degree between them.