Deep Learning for Social Media Data Analytics

Lieferzeit: Lieferbar innerhalb 14 Tagen

160,49 

Studies in Big Data 113

ISBN: 303110868X
ISBN 13: 9783031108686
Herausgeber: Tzung-Pei Hong/Leticia Serrano-Estrada/Akrati Saxena et al
Verlag: Springer Verlag GmbH
Umfang: x, 299 S., 21 s/w Illustr., 65 farbige Illustr., 299 p. 86 illus., 65 illus. in color.
Erscheinungsdatum: 19.09.2022
Auflage: 1/2023
Produktform: Gebunden/Hardback
Einband: Gebunden

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.

Artikelnummer: 5939875 Kategorie:

Beschreibung

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.

Herstellerkennzeichnung:


Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

E-Mail: juergen.hartmann@springer.com

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