| Lerninhalte |
Computational Social Science (CSS) is a scientific discipline where computational tools and techniques are used to answer research questions that could be social in nature. This area has attracted two groups of scientists: a) social scientists with computational skills, and b) computational scientists with an interest in questions related to social phenomena. The advent of social media and online social networks has led to increased digitization of human interactions and exponential growth of the digital traces left behind in these interactions. Both of these groups of scientists know the value of these digital trace data and have the skills to analyze them. Generally, research projects, including in CSS, could follow two main approaches: data-driven (inductive) or theory-driven (deductive). While the data-driven approach might start from an inductive or bottom-up exploration of the data to find general patterns, the theory-driven approach -predominantly used in social sciences- usually starts with a question and follows a few steps: Question and theorize, Gather and pre-process data, Model, and Report and publish.
By the end of this course, students will be familiar with deductive and inductive approaches to research. They will have knowledge of different research questions, data sources and possible answers for the questions provided in Computational Social Science. They will also learn about ethics in research using digital trace data.
Einführende Literatur:
- Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press
- Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
- Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170
- Kashyap, R., Rinderknecht, R. G., Akbaritabar, A., Alburez-Gutierrez, D., Gil-Clavel, S., Grow, A., Kim, J., Leasure, D. R., Lohmann, S., Negraia, D. V., Perrotta, D., Rampazzo, F., Tsai, C.-J., Verhagen, M. D., Zagheni, E., & Zhao, X. (2023). Digital and computational demography. In Research Handbook on Digital Sociology (pp. 48–86). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781789906769/book-part-9781789906769-10.xml preprint is openly accessible here: https://osf.io/preprints/socarxiv/7bvpt
- Macy, M. W., & Willer, R. (2002). From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology, 28(1), 143–166. https://doi.org/10.1146/annurev.soc.28.110601.141117
- Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojevic, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., & Barabási, A.-L. (2018). Science of science. Science, 359(6379), eaao0185. https://doi.org/10.1126/science.aao0185
- Cioffi-Revilla, C. (2017). Introduction to Computational Social Science. Springer International Publishing. https://doi.org/10.1007/978-3-319-50131-4
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