Mining Lurkers in Online Social Networks
Roberto Interdonato, Andrea Tagarelli
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Springer International Publishing
Naturwissenschaften, Medizin, Informatik, Technik / Datenkommunikation, Netzwerke
Beschreibung
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 .
Kundenbewertungen
ranking, evolutionary game theory, centrality, influence maximization, time-evolving network model, information diffusion, vicarious learning, lurking behavior analysis, passive users, user engagement, graph mining, lurkers, trust networks, multiplex network model, network science, influence propagation, online social networks, silent users