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Influence and behavior analysis in s...

Kaya, Mehmet.

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Influence and behavior analysis in social networks and social media

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Influence and behavior analysis in social networks and social media/ edited by Mehmet Kaya, Reda Alhajj.
其他作者:	Kaya, Mehmet.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	vi, 235 p. :ill., digital ;24 cm.
內容註:	Social network to improve the educational experience with the deployment of different learning models -- Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods -- Traits of Leaders in Movement Initiation:Classification and Identification -- Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube -- Diffusion Algorithms in Multimedia Social Networks: a novel model -- Analyzing Twitter Accounts of Shaw Communications -- Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia -- Incentivized Social Sharing: Characteristics and Optimization -- Rumor Source Detection in Finite Graphs with Boundary Effects by Message-passing Algorithms -- Robustness of Influence Maximization against Non-Adversarial Perturbations -- Analyzing Social Book Reading Behavior on Goodreads and how it predicts Amazon Best Sellers.
Contained By:	Springer eBooks
標題:	Online social networks. -
電子資源:	https://doi.org/10.1007/978-3-030-02592-2
ISBN:	9783030025922

Influence and behavior analysis in social networks and social media
Influence and behavior analysis in social networks and social media[electronic resource] /edited by Mehmet Kaya, Reda Alhajj. - Cham :Springer International Publishing :2019. - vi, 235 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..

Social network to improve the educational experience with the deployment of different learning models -- Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods -- Traits of Leaders in Movement Initiation:Classification and Identification -- Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube -- Diffusion Algorithms in Multimedia Social Networks: a novel model -- Analyzing Twitter Accounts of Shaw Communications -- Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia -- Incentivized Social Sharing: Characteristics and Optimization -- Rumor Source Detection in Finite Graphs with Boundary Effects by Message-passing Algorithms -- Robustness of Influence Maximization against Non-Adversarial Perturbations -- Analyzing Social Book Reading Behavior on Goodreads and how it predicts Amazon Best Sellers.

This timely book focuses on influence and behavior analysis in the broader context of social network applications and social media. Twitter accounts of telecommunications companies are analyzed. Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified. The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17) Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis.

ISBN: 9783030025922

Standard No.: 10.1007/978-3-030-02592-2doiSubjects--Topical Terms:

624374
Online social networks.

LC Class. No.: HM742 / .I545 2019

Dewey Class. No.: 302.30285

Influence and behavior analysis in social networks and social media
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520 $a This timely book focuses on influence and behavior analysis in the broader context of social network applications and social media. Twitter accounts of telecommunications companies are analyzed. Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified. The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17) Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis.
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