Intersection of Machine Learning and Computational Social Sciences

The text discusses theoretical background of algorithms and applications of methods using social science problems. It explores different machine-learning approaches to tackle the current issues in the digital world by analyzing social networks. It discusses topics such as principles of semi-supervised learning, and reinforcement algorithms.

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. Features: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. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.