GESIS supports the growing interdisciplinary community of Computational Social Science by offering training courses in data science and CSS methods and by providing training materials on central CSS topics and methods.
Among the courses and workshops we run as GESIS trainings are: introduction to R for data analysis, introduction to computational social science with Python/ R, workshops on using social media for social science research, and a fall seminar in computational social science. All upcoming courses can be found here.
Please also consider the selection of our introductory talks, teaching materials, videos and Jupyter notebooks.
Social ComQuant is a European Commission funded project (2020-23). GESIS and the ISI Foundation are twinned with Koç University, Turkey, to establish an infrastructure to enhance teaching and research capacities in the areas of computational and quantitative social sciences.
2017–2019 we held three editions of the Summer School on Methods in Computational Social Science, funded by the Volkswagen Foundation. A collection of lectures and talks given during these summer schools are publicly available via YouTube.
Please tune in for Milena Tsvetkova, Ancsa Hannák, Tina Eliassi-Rad, Bruno Ribeiro, Dirk Helbing, Rossano Schiffanella, Alessandro Vespignani, Ciro Cattuto, Andrea Baronchelli, Andreu Casas, Miriam Redi, Yelena Mejova, Jackelyn Hwang, Luca Maria Aiello, Mauro Martino, Sandra González-Bailón, Maximilian Schich, Björn W. Schuller.
GESIS has established a Google Group as a digital forum for exchanging ideas, offering jobs, announcing events, and keeping in touch. Researchers from all disciplines are most welcome to join:
The GESIS Computational Social Science (CSS) Seminar is an event for expert exchange on data science and social analytics. It is held at GESIS, Cologne, and open to all interested.
Please find more information here.
- 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
- 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). SOCIAL SCIENCE: Computational Social Science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
- Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
- Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. https://doi.org/10.1126/science.346.6213.1063
- Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
- Sen, I., Flöck, F., Weller, K., Weiß, B., & Wagner, C. (2021). A Total Error Framework for Digital Traces of Human Behavior on Online Platforms. Public Opinion Quarterly, 85(S1), 399–422. https://doi.org/10.1093/poq/nfab018
- Snee, H., Hine, C., Morey, Y., Roberts, S., & Watson, H. (2016). Digital Methods as Mainstream Methodology: An Introduction. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital Methods for Social Science (p. 1–11). Palgrave Macmillan UK. https://doi.org/10.1057/9781137453662_1
- Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021). Measuring algorithmically infused societies. Nature, 595(7866), 197–204. https://doi.org/10.1038/s41586-021-03666-1
- Engel, U., Quan-Haase, A., Liu, S. X., & Lyberg, L. (Eds.). (2022). Handbook of computational social science: Volume 1: Theory, case studies and ethics; Volume 2: Data science, statistical modelling, and machine learning methods. Routledge, Taylor & Francis Group.