Deep Learning for Social Media Data Analytics (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Big Data, 113
Release date:	September 2022
First published:	2022
Editors:	Tzung-Pei Hong • Leticia Serrano-Estrada • Akrati Saxena • Anupam Biswas
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	299
Edition:	1st ed. 2022
ISBN-13:	978-3-03-110868-6
Categories:	Books > Reference & Interdisciplinary > Communication studies > Media studies Books > Professional & Technical > Energy technology & engineering > Electrical engineering > General Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-110868-X
Barcode:	9783031108686

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