Chapman and Hall/CRC Social Network Analytics: Empowering Data Engineering with Deep Learning and Large Language Models

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 book Explores 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.