Mining Lurkers in Online Social Networks
Principles, Models, and Computational Methods
Authors: Tagarelli, Andrea, Interdonato, Roberto
Free Preview- 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
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- About this book
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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 .
- Table of contents (9 chapters)
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Introduction
Pages 1-5
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Background
Pages 7-13
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Characterization and Ranking of Lurkers
Pages 15-27
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Lurking Behavior Analysis
Pages 29-38
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Pervasiveness of the Notion of Lurking in OSNs
Pages 39-46
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Table of contents (9 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Mining Lurkers in Online Social Networks
- Book Subtitle
- Principles, Models, and Computational Methods
- Authors
-
- Andrea Tagarelli
- Roberto Interdonato
- Series Title
- SpringerBriefs in Computer Science
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s), under exclusive license to Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-00229-9
- DOI
- 10.1007/978-3-030-00229-9
- Softcover ISBN
- 978-3-030-00228-2
- Series ISSN
- 2191-5768
- Edition Number
- 1
- Number of Pages
- VI, 93
- Number of Illustrations
- 1 b/w illustrations, 9 illustrations in colour
- Topics
