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. Please register for upcoming courses at GESIS.
Please also consider the selection of our introductory talks, teaching materials, videos and Jupyter notebooks.
GESIS online talk series.
More information on the MTE series, on upcoming and past talks. Please find all videos on digital behavioral data and computational methods on our YouTube channel: CSS Playlist
A Short Introduction to Computational Social Science and Digital Behavioral Data – by Katrin Weller
MTE Talk | Slides (2.55 MB)
Digital Traces of Human Behavior from Online Platforms – Research Designs and Error Sources – by Indira Sen and Fabian Flöck
MTE Talk | Slides (4.16 MB) | Video | Tutorial | Paper | Extended Paper
Combining Survey Data and Digital Behavioral Data –
by Johannes Breuer and Sebastian Stier
MTE Talk | Slides (3.39 MB) | Guideline (1.60 MB) | Paper | Paper
Research Ethics and Data Protection in Social Media Research –
by Oliver Watteler and Katrin Weller
MTE Talk | Slides (3.31 MB)
International Conference on Computational Social Science 2017 (IC2S2-17)
GESIS proudly organised the 2017 edition of the International Conference on Computational Social Science (IC2S2-17) in Cologne.
Get profound insights into the research field by watching the keynotes from Ciro Cattuto, Ágnes Horvát, Dashun Wang, Ulrik Brandes, Cecilia Mascolo, Kathleen Carley, Justin Grimmer, Daniel Romero, Milena Tsvetkova, Matt Taddy, María Pereda, Jeff Hancock – you can find them all on our YouTube playlist.
Have a look at the full conference program (9.16 MB) and our Flickr albums that convey at least some of the great conference atmosphere we enjoyed!
Social ComQuant
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.
Summer School on Methods in Computational Social Science
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.
Networking: CSSNET Google Group
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:
https://groups.google.com/g/CSSNET
Expert Exchange: CSS Seminar
The GESIS Computational Social Science (CSS) Seminar is an event series for expert exchange on data science and social analytics. It is held at GESIS, Cologne, and open to all interested. Due to the pandemic situation talks are now being organized as online events.
Please find information on upcoming talks.
- 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. 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. https://doi.org/10.4324/9781003024583; https://doi.org/10.4324/9781003025245