| Beschreibung |
How can we use data to answer questions about people and society? People leave digital traces as they search, post and move. This course introduces computational social science, which uses such traces, together with surveys and other kinds of data, to learn about people and behaviour. It pairs the concepts of empirical social research with data-science methods. The course moves from questions to concepts and measurement, then to collecting data by asking people (surveys) and by observing them (digital traces, APIs and web scraping), to describing and visualizing data, interpreting texts by hand and by machine (text as data, large language models), analysing networks, and the ethics of working with people's data. Toxicity, democracy and news run as examples through the term. In weekly exercise sessions, students work in Python with real data. By the end of the course, students will be able to turn a social question into concepts and measures, collect and analyse their own data, and judge what kind of claim a dataset can support. In a mini-project, they take one question about their own data from start to finish. |
| Literatur |
Bhattacherjee, A. Social Science Research: Principles, Methods and Practices (revised ed.)
Salganik, M. J. Bit by Bit: Social Research in the Digital Age. Princeton University Press, 2018.
Van Atteveldt, W., Trilling, D., & Arcíla Calderón, C. Computational Analysis of Communication. Wiley, 2022 |
