Full Description
This book explores the evolution of social network analysis and mining (SNAM), a field that originated in social and business communities but has expanded significantly in recent years. The rise of online social platforms, email logs, phone records, and instant messaging systems has driven the development of advanced techniques for analyzing social networks, drawing heavily on graph theory and machine learning.
As the Web increasingly becomes a social medium, it fosters human interaction, the sharing of experiences and knowledge, and the formation and evolution of communities. This transformation has amplified the importance of SNAM in fields such as academia, politics, homeland security, and business, where understanding the complex relationships between networked actors is crucial.
This book presents a comprehensive collection of cutting-edge research and developments in SNAM, offering a valuable resource for researchers and practitioners seeking to deepen their understanding of social networks and their applications.
Contents
Targets of Terrorgram: The Who, What, and Where of Threatening Communication on Terrorgram.- Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China.- When Words Become Warnings: Assessing Threats in Online Spaces.- Modelling effects of social network topology on opinion dynamics during the COVID-19 pandemic.- Explainable Data-Driven Digital Twin for Stress Management.- Exploring Gender-Specific Symptoms in Coronary Heart Disease Diagnosis.- Enhancing Explainability in Knowledge Graph Construction for Healthcare Services Using Large Language Models.- Fuzzy Consensus Clustering for Deep Learning Tuning by using Medical Diagnosis as a case.- Mislabeling Misinformation: Annotation Consistency Shapes Machine Learning for DIY Health Risks.- On the Use of 3D Modeling, Reconstruction and Printing Techniques for the Development of an Ankle Bone Prosthesis.- Therapist by Chance: Investigating ChatGPT's Emotional and Mental Health Support via Sentiment Analysis on Social Networks.
