© 2018

Mining Lurkers in Online Social Networks

Principles, Models, and Computational Methods


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

Table of contents

  1. Front Matter
    Pages i-vi
  2. Andrea Tagarelli, Roberto Interdonato
    Pages 1-5
  3. Andrea Tagarelli, Roberto Interdonato
    Pages 7-13
  4. Andrea Tagarelli, Roberto Interdonato
    Pages 15-27
  5. Andrea Tagarelli, Roberto Interdonato
    Pages 29-38
  6. Andrea Tagarelli, Roberto Interdonato
    Pages 39-46
  7. Andrea Tagarelli, Roberto Interdonato
    Pages 47-65
  8. Andrea Tagarelli, Roberto Interdonato
    Pages 67-76
  9. Andrea Tagarelli, Roberto Interdonato
    Pages 77-85
  10. Andrea Tagarelli, Roberto Interdonato
    Pages 87-93

About this book


This SpringerBrief  brings order  to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs)  lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs.

 All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate.

 Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining.

 While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields.  Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and  human-computer interaction will also find this brief useful research material . 


lurking behavior analysis centrality influence propagation lurkers silent users passive users vicarious learning user engagement centrality ranking online social networks information diffusion influence propagation influence maximization evolutionary game theory time-evolving network model multiplex network model network science trust networks graph mining

Authors and affiliations

  1. 1.DIMES, Cubo 42C, Piano 5University of CalabriaArcavacata di RendeItaly
  2. 2.UMR TETISCIRADMontpellierFrance

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