Given a set of peers with overlapping interests where each peer wishes to keep track of new documents that are relevant to their interests, we propose Shrack-a self-organizing peer-to-peer (P2P) system for document sharing and tracking. The goal of a document-tracking system is to disseminate new documents as they are published. We present a framework of Shrack and propose a gossip-like pull-only information dissemination protocol. We explore and develop mechanisms to enable a self-organizing network, based on common interest of document sets among peers. Shrack peers collaboratively share new documents of interest with other peers. Interests of peers are modeled using relevant document sets and are represented as peer profiles. There is no explicit pro file exchange between peers and no global information available. We describe how peers create their user pro files, discover the existence of other peers, locally learn about interest of other peers, and finally form a self-organizing overlay network of peers with common interests. Unlike most existing P2P file sharing systems which serve their users by finding relevant documents based on an instant query, Shrack is designed to help users that have long-term interests to keep track of relevant documents that are newly available in the system. The framework can be used as an infrastructure for any kind of documents and data, but in this thesis, we focus on research publications. We built an event-driven simulation to evaluate the performance and behaviour of Shrack. We model simulated users associated with peers after a subset of authors in the ACM digital library metadata collection. The experimental results demonstrate that the Shrack dissemination protocol is scalable as the network size increases. In addition, self-organizing overlay networks, where connections between peers are based on common interests as captured by their associated document sets, can help improve the relevance of documents received by peers in terms of F-score over random peer networks. Moreover, the resulting self-organizing networks have the characteristics of social networks.