Social Network Analytics : Empowering Data Engineering with Deep Learning and Large Language Models
Book Details
Format
Hardback or Cased Book
ISBN-10
104100690X
ISBN-13
9781041006909
Publisher
Taylor & Francis Ltd
Imprint
CRC Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Sep 3rd, 2026
Print length
236 Pages
Product Classification:
Computational and corpus linguisticsComputational linguisticsElectrical engineeringAutomatic control engineeringCommunications engineering / telecommunicationsAlgorithms & data structuresAlgorithms and data structuresData miningComputer networking & communicationsComputer networking and communicationsComputer architecture & logic designComputer architecture and logic designArtificial intelligenceArtificial intelligence (AI)
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This book presents the cutting-edge techniques of social network analytics, focusing on both the positive and negative aspects of social media.
This book presents the cutting-edge techniques of social network analytics, focusing on both the positive and negative aspects of social media. While platforms like X, Facebook, and LinkedIn serve as powerful tools for product promotion and crisis management, they also present challenges such as the spread of misinformation, cyberbullying, and hateful content. This book explores these dimensions while highlighting the advancements in social media analytics, specifically through the lens of emerging technologies like artificial intelligence, machine learning, and deep learning. This book is intended for data engineers, researchers, practitioners, and students in the fields of data science, social computing, and artificial intelligence. This bookExplores state-of-the-art deep learning methodologies tailored for social network analysis, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) to uncover hidden patterns and trends within social media data. Examines the application of large language models, such as GPT (Generative Pre-trained Transformer), in analysing and generating text-based content. Readers will gain practical insights into using these models for content generation, summarisation, and classification tasks. Provides detailed coverage of sentiment analysis techniques, enabling readers to extract valuable insights from user-generated content, helping organisations better understand public opinion. Explores methodologies for detecting communities within social networks, uncovering hidden structures, relationships, and influential nodes or communities. Offers insights into predicting user behaviour on social media platforms, including engagement, preferences, and click-through rates, equipping readers with tools to drive informed decision-making.
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