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Mining lurkers in online social netw...

Interdonato, Roberto.

Mining lurkers in online social networksprinciples, models, and computational methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mining lurkers in online social networksby Andrea Tagarelli, Roberto Interdonato.
Reminder of title:	principles, models, and computational methods /
Author:	Tagarelli, Andrea.
other author:	Interdonato, Roberto.
Published:	Cham :Springer International Publishing :2018.
Description:	vi, 93 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-00229-9
ISBN:	9783030002299$q(electronic bk.)

Mining lurkers in online social networksprinciples, models, and computational methods /
Tagarelli, Andrea.

Mining lurkers in online social networksprinciples, models, and computational methods /[electronic resource] :by Andrea Tagarelli, Roberto Interdonato. - Cham :Springer International Publishing :2018. - vi, 93 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

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.

ISBN: 9783030002299$q(electronic bk.)

Standard No.: 10.1007/978-3-030-00229-9doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Mining lurkers in online social networksprinciples, models, and computational methods /
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