Computational Social Science: Principles and Applications

Computational thinking and methods have been widely discussed and adopted by social scientists in various subject areas (e.g., anthropology, communication, political science, public health, and sociology). This course is about how computational social science (CSS), as an emerging paradigm of research, changes the way in which social scientists empirically observe and understand human society. The course is composed of three modules:

• The first module focuses on the fundamental principles in CSS, including research design, implementation, data collection and management, and data analysis.
• The second module focuses on the conceptualization and modeling of three types of data preeminent in CSS research: text, time, and structure.
• The third module concentrates on the application of computational methods in some prominent research domains, such as health communication, political communication, and user analytics.

Textbook

Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.

Weekly Topics

• Computational social science: A buzzword or something else?
• Big Data = Quality Data?
• Do we need to sing old songs? Research Design in CSS
• Correlation, Causation and/or Prediction
• Data, Algorithms, and Society
• Language on social media (I)
• Language on social media (II)
• What does time mean in (computational) social science?
• Individuals and structures