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.
Our definition of lurking is substantially consistent with the various existing perspectives on lurking, previously mentioned in the Introduction. It can in general recognize and measure behaviors that rely on phenomena of lack of information production (i.e., inactivity or occasional activity) as well as on phenomena of information hoarding or overconsumption, like free-riding and leeching. It is worth emphasizing that taking into account the authoritativeness of the information received as well as the non-authoritativeness of the information produced by lurkers is essential to the correct scoring of lurkers. Therefore, our definition of lurking can also explain more complex perspectives, such as legitimate peripheral participation. In this case, a lurker is regarded as a novice, for which it's legitimate to learn from experts as a form of cognitive apprenticeship. Indeed, by applying our LurkerRank methods, in @cite_52 we have addressed an exemplary form of legitimate peripheral participation, known as vicariously learning, in the context of research collaboration networks.
@cite_52
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Despite being a topic of growing interest in social learning theory, vicarious learning has not been well-studied so far in digital library related tasks. In this paper, we address a novel ranking problem in research collaboration networks, which focuses on the role of vicarious learner. We introduce a topology-driven vicarious learning definition and propose the first centrality method for ranking vicarious learners. Results obtained on DBLP networks support the significance and uniqueness of the proposed approach.