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Full Description
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. The book explores these dimensions while highlighting the advancements in social media analytics, specifically through the lens of emerging technologies like AI, 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.
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.
Contents
The Role of Artificial Intelligence in Social Network Analysis. 2. Unraveling the Impact of Social Networks: A Comprehensive Overview. 3. Understanding factors leading to local and global spatial spread in social media. 4. Understanding Deep Learning and LLMs for Detecting Depression in Social Media Posts. 5. Deep Learning and Large Language Models for Detecting Depression in Social Media Posts - An Indian Context. 6. Identifying Hate and Offensive Content using Multimodal Deep Learning. 7. Personalised Advertising, Customer Segmentation through Social Network Analytics. 8. Role of Digital Agriculture in Shaping the Future of Farming: A Social Network Analytics Approach. 9. Explainable Transfer Learning Model for Disaster Damage Assessment from Social Media Images. 10. Understanding and Detecting Online Homophobia and Transphobia in Low-Resource Indian Languages: A Focus on Kannada and Telugu. 11. Sarcasm Detection in Code-Mixed Social Media Posts: A Hybrid Perspective.
