About the course
This course provides students with an extensive exposure to the fundamental principles and essential techniques of computational social science methods, ranging from automatic collection of digital and online data to machine learning with or without human supervision. The methods are intended to complement and enhance the traditional social science methods of data collection and analysis, such as survey, experiment, content analysis, and statistical analysis. Topics include opportunities and challenges for computational social science research in the digital age, descriptive/predictive vs. explanatory research, found data versus made data, research design, causal inference, sampling of social media, online experiment, behavioural analytics, text mining, and online research ethics. The course is useful for students who are interested in using computational methods for social, cultural, business, legal, and other areas of research.
Compulsory Readings 1. Cioffi-Revilla, C. (2017). Introduction to computational social science: Principles and applications, 2nd ed. Springer. 2. Salganik, M. (2018). Bit by bit: Social research in the digital age. Princeton University Press. 3. Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. Sage. 2.2. Additional Readings 1. Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ... & Jebara, T. (2009). Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915), 721. 2. Watts, D. J. (2013). Computational social science: Exciting progress and future directions. The Bridge on Frontiers of Engineering, 43(4), 5-10. 3. Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129-152. 4. Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6-13. 5. Ackland, R., & Zhu, J. J. (2015). Social network analysis. In Innovations in digital research methods. SAGE Publications. 6. Liang, H., & Zhu, J. J. H. (2017). Big data, collection of (social media, harvesting). In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), International Handbook of Communication Methods, Wiley & Sons.