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" Mining lurkers in online social networks : "
Andrea Tagarelli, Roberto Interdonato.
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BL
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| Record Number
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859124
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| Main Entry
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Tagarelli, Andrea,1976-
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| Title & Author
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Mining lurkers in online social networks : : principles, models, and computational methods /\ Andrea Tagarelli, Roberto Interdonato.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2018]
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| Series Statement
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SpringerBriefs in computer science,
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1 online resource (vi, 93 pages) :: illustrations
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| ISBN
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3030002292
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: 9783030002299
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3030002284
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9783030002282
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Includes bibliographical references.
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| Contents
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Intro; Contents; 1 Introduction; 1.1 Organization of this brief; 1.2 Audience and scope; 1.3 Thanks; References; 2 Background; 2.1 Perception of lurking; 2.2 How to identify lurkers?; 2.3 Why do lurkers act?; 2.4 How to promote delurking?; 2.5 Lurking as a computational problem; References; 3 Characterization and ranking of lurkers; 3.1 Topology-driven lurking definition; 3.2 The LurkerRank family of ranking algorithms; 3.3 Time-aware LurkerRank algorithms; 3.3.1 Freshness and activity trend; 3.3.2 Time-static LurkerRank; 3.3.3 Time-Evolving LurkerRank; 3.4 Learning to rank lurkers; References
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4 Lurking behavior analysis; 4.1 Significance and effectiveness of LurkerRank; 4.2 Do lurkers match inactive users?; 4.3 Do lurkers match newcomers?; 4.4 How frequently do lurkers respond to others' actions?; 4.5 Do lurkers create preferential relations with active users?; 4.6 How do lurking trends evolve?; 4.7 How do topical interests of lurkers evolve?; References; 5 Pervasiveness of the notion of lurking in OSNs; 5.1 Lurking and collaboration networks; 5.1.1 Vicarious-learning-oriented RCNs; 5.1.2 The VLRank method; 5.2 Lurking and trust contexts
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5.2.1 TrustRank-biased LurkerRank; 5.2.2 Lurking and trustworthiness in ranking problems; 5.2.3 Lurking and data privacy preservation; References; 6 Delurking; 6.1 User engagement in online social networks; 6.2 Self-delurking randomization; 6.3 Delurking and influence propagation; 6.3.1 Information diffusion and influence maximization; 6.3.2 Delurking-oriented targeted influence maximization; 6.3.3 Community-based delurking; 6.3.4 Diversity-aware delurking; 6.3.4.1 Diversity in information spreading; 6.3.4.2 Diversity-integrated targeted influence maximization; References
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7 Boundary spanning lurking; 7.1 Lurkers across communities; 7.1.1 Across-community boundary spanning vs. within-community centrality; 7.1.2 Lurkers vs. community-based bridge users; 7.2 Cross-OSN analysis of alternate behaviors of lurking and active participation; 7.2.1 Multilayer network model; 7.2.2 Multilayer LurkerRank; 7.2.3 Multilayer alternate lurker-contributor ranking; References; 8 Bringing lurking in game theory; 8.1 The lurker game; 8.1.1 Basic dynamics; 8.1.2 Mean field analysis; 8.1.3 Rewarding mechanisms; 8.2 Lurker game on networks; References
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9 Concluding remarks and challenges; 9.1 Modeling lurking behaviors through latent interactions; 9.2 Emotion-driven analysis of lurkers; 9.3 Psycho-sociological influences on lurkers; 9.4 Dis/misinformation, fake news, and lurkers; References
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| Abstract
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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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Data mining.
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Online social networks.
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Computer networking communications.
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COMPUTERS-- General.
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Data mining.
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Network hardware.
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Online social networks.
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Society social sciences.
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| Dewey Classification
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006.312
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| LC Classification
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QA76.9.D343
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| Added Entry
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Interdonato, Roberto
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