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 The GESIS 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 listed below.
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)
- 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