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The massive presence of silent members in online communities, the so-calledlurkers, has long attracted the attention of researchers in social science,cognitive psychology, and computer-human interaction. However, the study oflurking phenomena represents an unexplored opportunity of research in datamining, information retrieval and related fields. In this paper, we take afirst step towards the formal specification and analysis of lurking in socialnetworks. We address the new problem of lurker ranking and propose the firstcentrality methods specifically conceived for ranking lurkers in socialnetworks. Our approach utilizes only the network topology without probing intotext contents or user relationships related to media. Using Twitter, Flickr,FriendFeed and GooglePlus as cases in point, our methods' performance wasevaluated against data-driven rankings as well as existing centrality methods,including the classic PageRank and alpha-centrality. Empirical evidence hasshown the significance of our lurker ranking approach, and its uniqueness ineffectively identifying and ranking lurkers in an online social network.

Lurking in Social Networks: Topology-based Analysis and Ranking Methods