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Mining Lurkers in Online Social Networks: Principles, Models, and Computational Methods: SpringerBriefs in Computer Science

Autor Andrea Tagarelli, Roberto Interdonato
en Limba Engleză Paperback – 19 noi 2018
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 . 

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Specificații

ISBN-13: 9783030002282
ISBN-10: 3030002284
Pagini: 88
Ilustrații: VI, 93 p. 10 illus., 9 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Computer Science

Locul publicării:Cham, Switzerland

Caracteristici

First book to discuss the lurking behavior phenomenon in online social networks under both social science and computational perspectives It provides detailed descriptions of computational approaches that enable understanding and mining of lurking behaviors, including centrality and ranking, influence propagation and maximization for user engagement, cross-platform analysis methods, and evolutionary games It paves the way for next-generation models and techniques that can cope with a large, previously unexplored set of related problems and applications in social science, network science, and other information science related fields