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020 |a9789811573767|9978-981-15-7376-7
024 7 |a10.1007/978-981-15-7376-7|2doi
041 |aeng
049 |aTürk Dil Kurumu Kütüphanesi
050 4|aHM741-743
072 7|aJFD|2bicssc
072 7|aSOC052000|2bisacsh
072 7|aJBCT1|2thema
082 04|a302.231|223
090 |aEK/4335
100 1 |aShi, Juan.|eauthor.|4aut|4http://id.loc.gov/vocabulary/relators/aut
245 10|aIndividual Retweeting Behavior on Social Networking Sites|h[electronic resource] :|bA Study on Individual Information Disseminating Behavior on Social Networking Sites /|cby Juan Shi, Kin Keung Lai, Gang Chen.
250 |a1st ed. 2020.
264 1|aSingapore :|bSpringer Nature Singapore :|bImprint: Springer,|c2020.
300 |aXVII, 132 p. 38 illus., 20 illus. in color.|bonline resource.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
347 |atext file|bPDF|2rda
505 0 |a1. Introduction -- 2. Research Background and Research Questions -- 3. Literature Review and Theoretical Foundation -- 4. Research Scheme Design -- 5. Dominating Factors Affecting Individual Retweeting Behavior -- 6. Direct Effect and Mediating Effect of Individual Retweeting Behavior on SNS -- 7. Moderating Effect of Individual Retweeting Behavior on SNS -- 8. Conclusion and Discussion.
520 |aThis book explores and analyzes influential predictors and the underlying mechanisms of individual content sharing/retweeting behavior on social networking sites (SNS) from an empirical perspective. Since Individual content sharing/ retweeting behavior expedites information dissemination, it is a critical mechanism of information diffusion on Twitter. Individual sharing/retweeting behavior does not appear to happen randomly. So, what factors lead to individual information dissemination behavior? What are the dominating predictors? How does the recipient make retweeting decisions? How do these influential predictors combine and by what mechanism do they influence an individual’s retweeting decisions? Furthermore, are there any differences in the process of individual retweeting decisions? If so, what causes such differences? In order to answer these previously unexplored questions and gain a holistic view of individual retweeting behavior, the authors examined people’s retweeting history on Twitter and obtained a real dataset containing more than 60 million Twitter posts. They then employed text mining and natural language processing techniques to extract useful information from social media content, and used various feature selection methods to identify a subset of salient features that have substantial effects on individual retweeting behavior. Lastly, they applied the Elaboration Likelihood Model to build an overarching theoretical framework to reveal the underlying mechanisms of individual retweeting behavior. Given its scope, this book will appeal to researchers interested in investigating information dissemination on social media, as well as to marketers and administrators who plan to use social networking sites as an important avenue for information dissemination.
650 0|aSocial media.
650 14|aSocial Media.
700 1 |aLai, Kin Keung.|eauthor.|0(orcid)0000-0003-0014-2095|1https://orcid.org/0000-0003-0014-2095|4aut|4http://id.loc.gov/vocabulary/relators/aut
700 1 |aChen, Gang.|eauthor.|4aut|4http://id.loc.gov/vocabulary/relators/aut
710 2 |aSpringerLink (Online service)
773 0 |tSpringer Nature eBook
776 08|iPrinted edition:|z9789811573750
776 08|iPrinted edition:|z9789811573774
776 08|iPrinted edition:|z9789811573781
856 40|uhttps://doi.org/10.1007/978-981-15-7376-7
912 |aZDB-2-LCM
912 |aZDB-2-SXL
950 |aLiterature, Cultural and Media Studies (SpringerNature-41173)
950 |aLiterature, Cultural and Media Studies (R0) (SpringerNature-43723)