Deep Learning for Social Media Data Analytics

This edited book explores the use of deep learning for social media data analytics, covering network structure analysis, text analysis, user behavior analysis, and security analysis. It offers valuable insights and innovative solutions for researchers and practitioners.

Format: Hardback
Length: 299 pages
Publication date: 19 September 2022
Publisher: Springer International Publishing AG

This edited book delves into the cutting-edge research exploring the theoretical and practical applications of deep learning in the realm of social media data analysis. With the abundance of diverse content on social networking platforms, the vast volumes of data they generate hold immense potential to shape various aspects of society, including business, politics, security, planning, and more. In recent years, deep learning techniques have achieved remarkable success across various domains in artificial intelligence. The research presented in this comprehensive volume stems from the belief that significant advancements can still be made in harnessing the power of deep learning for social media data analytics. Comprising fifteen chapters, the book is structured into four sections that cover original research in network structure analysis, social media text analysis, user behavior analysis, and social media security analysis. This work serves as a valuable resource for researchers seeking to deepen their understanding of the field, as well as practitioners looking to implement innovative deep learning solutions in their social media data analytics endeavors.

Weight: 635g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031108686
Edition number: 1st ed. 2022