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Lurking in social networks: topology-based analysis and ranking methods

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Abstract

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.

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Notes

  1. Experiments were carried out on an Intel Core i7-3960X CPU @ 3.30GHz, 64GB RAM machine.

  2. Inferring and modeling trust in OSNs is a challenging topic per se: more refined alternatives to our entropy-based inference of trust can certainly be found.

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Authors and Affiliations

  1. Department Computer Engineering, Modeling, Electronics, and Systems Sciences, University of Calabria, Rende (CS), Italy

    Andrea Tagarelli & Roberto Interdonato

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  1. Andrea Tagarelli
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  2. Roberto Interdonato
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Correspondence to Andrea Tagarelli.

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An abridged version of this paper appeared in Tagarelli and Interdonato (2013b).

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Tagarelli, A., Interdonato, R. Lurking in social networks: topology-based analysis and ranking methods. Soc. Netw. Anal. Min. 4, 230 (2014). https://doi.org/10.1007/s13278-014-0230-4

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