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In a recent study, we proposed a trusted gossip protocol for rumor resistant information sharing in peer-to-peer networks. While trust aware gossiping significantly reduced the rumor spread on the network, we observed that the random message spraying in trusted gossip creates too many redundant messages increasing the message overhead and error rate. In this paper, we propose a message targeting scheme that can significantly improve the performance of the trusted gossip. Our targeting scheme can be easily implemented in a social network setting. We performed large-scale simulations using traces collected from the Flickr social network and other data sets to estimate the performance of targeting in trusted gossip. Our experiments show that significant performance gains can be achieved.