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