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Characterization and Ranking of Lurkers

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Mining Lurkers in Online Social Networks

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

In this chapter, we discuss computational approaches to identify and rank lurkers in online social networks. We begin with a formal definition of topology-driven lurking and a detailed description of a family of centrality methods specifically conceived for ranking lurkers solely based on network topology, namely LurkerRank. To better model dynamics of user behaviors, the Time-Aware LurkerRank models are also described. The chapter ends with the description of a learning-to-rank framework for lurker prediction and classification.

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Authors and Affiliations

  1. DIMES, Cubo 42C, Piano 5, University of Calabria, Arcavacata di Rende, Italy

    Andrea Tagarelli

  2. UMR TETIS, CIRAD, Montpellier, France

    Roberto Interdonato

Authors
  1. Andrea Tagarelli
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  2. Roberto Interdonato
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Tagarelli, A., Interdonato, R. (2018). Characterization and Ranking of Lurkers. In: Mining Lurkers in Online Social Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-00229-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-00229-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00228-2

  • Online ISBN: 978-3-030-00229-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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