Capacity Building in Computational Social Science

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.

GESIS online talk 
series.

More information
on the MTE series, on
upcoming and past talks.
Please find all videos 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

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)

Online Data Acquisition

by Roberto Ulloa

MTE Talk | Slides (6.41 MB)

Altmetrics

by Katrin Weller & Olga Zagovora

MTE Talk | Slides (5.41 MB)

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.

Project site

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.  

 Videos on Youtube | Event Website

Gathering Knowledge from User Generated Content

Lecture by Claudia Wagner

Video on YouTube

Reading Cross-cultural Relations from Wikipedia 

Lecture by Fabian Flöck

Video on YouTube

Data Scraping mit Python


Tutorial by Carsten Schwemmer

Video on YouTube (in German)

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 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.