Deep Learning for Social Media Data Analytics: 113 - Rilegato

Rilegato

ISBN 10: 303110868X ISBN 13: 9783031108686

Casa editrice: Springer-Nature New York Inc, 2022

Riassunto

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.

 

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Dalla quarta di copertina

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.

 

Dettagli dell?edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2022
• ISBN 10: 303110868X
• ISBN 13: 9783031108686
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 312
• Redattore: Hong Tzung-pei, Serrano-estrada Leticia, Saxena Akrati, Biswas Anupam
• Contatto del produttore: non disponibile