Computational Social Science toolbox 3500-FAKANG-CSST
The purpose of the course is to equip students with enough computational literacy for them to be able to further develop their skills. The course serves as an overview of the field with a lot of hands-on exercises and a safe, friendly space for questions and discussions. We will cover key areas important for both research and business.
We warm-up with basic statistical analysis of data. Then a gold standard of an A/B test is introduced – something regularly used by biggest companies to refine their products and services. From there we go towards Bayesian strategies of dealing with uncertainty – you will learn how likely it is you will get a good job and why you really shouldn’t keep arguing with your narrow-minded uncle every Holiday.
Then we deal with social networks, which are key to understanding the structure our social worlds. You will learn about structural opportunities and constraints, influence, contagions, homophily, reciprocity, balance, power and reputations – all of which are network-based phenomena. We will also deal with semantic analyses.
You will also learn what NLP has to offer and how it can be used to enrich research and better use sociological analysis in practice, including data mining/scraping, cleaning, analysis and interpretation.
We top it all off with an introduction to agent-based modeling. This versatile tool will allow you to capture complex social phenomena via social mechanisms implemented into a computational form, recreate them on your computer and play out different scenarios – experiment with social reality in a synthetic way.
The learning outcomes are focused on understanding and elaboration. Some coding will be introduced and encouraged, but is not necessary to complete the course. Students will be invited to present some of their work or ideas for it, which will be integrated into the course flow. That way we focus on topics which are of interest to participants and help them develop most useful skills.
Rodzaj przedmiotu
Tryb prowadzenia
Koordynatorzy przedmiotu
Efekty kształcenia
K_W03 Has in-depth knowledge about social structures and selected social institutions as well as their interrelations
K_U02 Can critically select information and materials for academic work, using various sources in Polish and a foreign language as well as modern technologies
K_U03 Can independently form and verify judgments about the causes of selected social phenomena
K_U06 Can use a selected computer program for data analysis, including its advanced functions
K_U07 Can form an in-depth evaluation of the measures undertaken for the purpose of solving a social problem, based on knowledge and analytical skills acquired
K_U10 Can prepare a presentation of a selected problem or study in Polish and in a foreign language
K_K03 Can gather, find, synthesize and critically assess information about social sciences
K_K04 Can argue a thesis using scientific evidence
K_K09 Is open to various theoretical and methodological perspectives of social research
K_K10 Takes responsibility for planned and performed tasks
Kryteria oceniania
Oral exam (80%) consisting of 1 long, prepared answer (50%) and 2 short questions (15% each) based on contents of meetings, reading materials and prespecified list of topics.
Re-sit: test consisting of 20 questions covering course material (4% each = 80%).
Participation in discussions (20%).
Total: 1 or 2 + 3 = 100%.
Two permissible absences.
Grades:
51% - 60% - 3
61% - 70% - 3.5
71% - 80% - 4
81% - 90% - 4.5
91% - 100% - 5
Literatura
Reading examples (recommended selection will be agreed during the course):
Bianchi, F., & Squazzoni, F. (2015). Agent‐based models in sociology. WIREs Computational Statistics, 7(4), 284–306. https://doi.org/10.1002/wics.1356
Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford University Press.
D'Ignazio, C., & Klein, L. F. (2023). Data feminism. MIT press.
Fuhse, J., Stuhler, O., Riebling, J., & Martin, J. L. (2020). Relating social and symbolic relations in quantitative text analysis. A study of parliamentary discourse in the Weimar Republic. Poetics, 78, 101363.
Gill, J. (2006). Essential Mathematics for Political and Social Research (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511606656
Gill, J., & Bao, L. (2024). Bayesian Social Science Statistics: From the Very Beginning (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781009341189
Granovetter, M. (1983). The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, 1, 201. https://doi.org/10.2307/202051
Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A. S. A., Milles, A., Nabe-Nielsen, J., Polhill, J. G., Radchuk, V., Rohwäder, M.-S., Stillman, R. A., Thiele, J. C., & Ayllón, D. (2020). The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism. Journal of Artificial Societies and Social Simulation, 23(2), 7. https://doi.org/10.18564/jasss.4259
Hedström, P., & Bearman, P. (2009). The Oxford handbook of analytical sociology. Oxford university press.
Holme, P., & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97–125. https://doi.org/10.1016/j.physrep.2012.03.001
McLevey, J. (2021). Doing computational social science: a practical introduction. Sage.
Moss, S., & Edmonds, B. (2005). Sociology and Simulation: Statistical and Qualitative Cross‐Validation. American Journal of Sociology, 110(4), 1095–1131. https://doi.org/10.1086/427320
Pachur, T., Schooler, L. J., & Stevens, J. R. (2014). We’ll Meet Again: Revealing Distributional and Temporal Patterns of Social Contact. PLoS ONE, 9(1), e86081. https://doi.org/10.1371/journal.pone.0086081
Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction (Second edition). Princeton University Press.
Snijders, T. A. B., Van De Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60. https://doi.org/10.1016/j.socnet.2009.02.004
Valente, T. W. (2012). Network interventions. Science, 337(6090), 49-53.
Wasserman, S., Faust, K. (1994). Social network analysis: Methods and applications. The Press Syndicate of the University of Cambridge.
Documentation of NetLogo and R/Python packages.
Other relevant content.
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: