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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Für höhere Schule und Studium
Illustrationen
84
12 s/w Abbildungen, 84 farbige Abbildungen
VI, 235 p. 96 illus., 84 illus. in color.
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 18 mm
Gewicht
ISBN-13
978-3-030-02591-5 (9783030025915)
DOI
10.1007/978-3-030-02592-2
Schweitzer Klassifikation
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.
