Social Network Analytics

Empowering Data Engineering with Deep Learning and Large Language Models
1. Auflage 2026
ISBN: 978-1-041-00690-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 272 Seiten, Format (B × H): 156 mm x 234 mm

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.

Zielgruppe

Academic

Autoren/Hrsg.

Kumar, Abhinav

Roy, Pradeep Kumar

Tripathy, Asis Kumar

Wu, Yulei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Webprogrammierung
• Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

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.

Über Autor(innen)

Pradeep Kumar Roy

Pradeep Kumar Roy received a B. Tech degree in Computer Science and Engineering from BPUT University Odisha. He received his M. Tech and Ph.D. degrees in Computer Science and Engineering from the National Institute of Technology Patna in 2015 and 2018, respectively. He received a Certificate of Excellence for securing a top rank in the M. Tech course. He is currently an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Surat, Gujarat, India.

Asis Kumar Tripathy

Asis Kumar Tripathy is a Professor at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has more than ten years of teaching experience. He completed his Ph.D. from the National Institute of Technology, Rourkela. His areas of research interest include wireless sensor networks, cloud computing, the Internet of Things, and advanced network technologies. He has several publications in refereed journals, reputed conferences, and book chapters to his credit.

Abhinav Kumar

Abhinav Kumar is currently working as an Assistant Professor in the Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad (MNNIT Allahabad), Prayagraj, India. Prior to joining MNNIT Allahabad, he worked as an Assistant Professor in the Department of CSE at IIIT Surat and Siksha “O” Anusandhan Deemed to be University, Bhubaneswar, Odisha. He has obtained a Ph.D. degree in Computer Science & Engineering from the Department of CSE of the National Institute of Technology Patna, India.

Dr. Yulei Wu

Dr. Yulei Wu is an Associate Professor working across the Faculty of Engineering and the Bristol Digital Futures Institute, University of Bristol, UK. He is also affiliated with the Smart Internet Lab and is a member of the High-Performance Networks Research Group. He received his Ph.D. degree in Computing and Mathematics and B.Sc. (1st Class Hons.) degree in Computer Science from the University of Bradford, UK, in 2010 and 2006, respectively. Before joining the University of Bristol, Dr. Wu was working at the University of Exeter and the Chinese Academy of Sciences (CAS).