Intersection of Machine Learning and Computational Social Sciences

The text employs computational techniques and large-scale data analysis to study complex social phenomena and human behavior. It discusses diverse methodologies, including agent-based modeling, network analysis, natural language processing, and machine learning, to gain insights into topics ranging from social network dynamics and opinion formation to economic trends and public health crises.

• Discusses the theoretical background of each algorithm in detail and presents the applications of each method.
• Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.
• Explains the use of predictive modeling in computational social science and applications of computational social science.
• Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.
• Covers topics such as density-based spatial clustering of applications with noise, the role of clustering in computational social science, and clustering in network structure.

Ideal for readers interested in computing & it.