Influence and Behavior Analysis in Social Networks and Social Media

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 co...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Kaya, Mehmet (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Alhajj, Reda (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:Lecture Notes in Social Networks,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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490 1 |a Lecture Notes in Social Networks,  |x 2190-5428 
505 0 |a 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. 
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. 
650 0 |a Social sciences-Data processing. 
650 0 |a Social sciences-Computer programs. 
650 0 |a Application software. 
650 0 |a Motivation research (Marketing). 
650 0 |a Social media. 
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650 2 4 |a Consumer Behavior.  |0 http://scigraph.springernature.com/things/product-market-codes/513060 
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