Introduction to Computational Social Science 3700-MSNS-24-ICSS

The development of the Internet and social media opens a whole new world of possibilities for social scientists to track human behavior. The questions that were difficult to handle using traditional methods of data collection and analysis can now be addressed. Furthermore, the new possibilities allow for the formulation of new questions and tracking phenomena, which were impossible to follow before. However, the new sources of information require social scientists to work on the verge of social science and computer science. This new area is usually called computational social science. Therefore, social scientists need to learn what type of data is available out there, how to collect it, and how to analyze it. It does not necessarily mean that they need to learn computer science because they might cooperate with computer scientists, but at least they need to understand the basic concepts to be able to plan adequate research.

This course is an introduction to computational methods for social scientists, therefore, it will introduce basic concepts only. It will not cover advanced methods, techniques, and theories. During the course, the following topics will be introduced: available data sources, Natural Language Processing (NLP), network analysis, and computer simulations. However, the focus will be not on the technical aspect but on the possible applications for social scientists. Each topic will be illustrated with real-life examples.

At the end of the course, students will be able to understand basic concepts of computational social science, communicate with data scientists/computer scientists using adequate vocabulary, and foremost formulate research questions that can be addressed with computational methods and/or data extracted from existing web-based data sources.

Rodzaj przedmiotu

obowiązkowe

Założenia (opisowo)

No specific skills are required and no mathematical and statistical knowledge is expected. However, it is very important not to be afraid of computers. Furthermore, general analytical thinking will be very helpful.

Koordynatorzy przedmiotu

Mikołaj Biesaga

Efekty kształcenia

The student who will complete the course will have a basic understanding of computational methods that could be applied to social science research, such as Natural Language Processing, network analysis, and computer simulations. Moreover, they will be able to formulate research questions and plan research that uses methods and tools covered during the class.

By the end of the semester students should be able to:
 understand basic concepts of programming such as algorithm, branching, iteration, and ‘memory independent computing’;
 know and understand the syntax and semantics of Python programming language;
 know and can perform operations on different types of Python programming languages;
 can write simple functions in Python;
 can handle JSON files in Python;
 know how to use Google Colab workspace;
 understand the importance of writing readable and reproducible code;
 understand basic concepts of computational social science;
 communicate with data scientists/computer programmers etc. (using adequate vocabulary);
 understand the advantages, challenges, and limitations of computational methods in social sciences;
 formulate research questions that can be addressed with computational methods and/or data extracted from existing web-based data sources;
 plan research using computational methods (especially webscraping, web API data extraction, and natural language processing);
 use materials from the course to scrap a website, work with a simple API, and perform basic Natural Language Processing.

Kryteria oceniania

The final grade will be determined based on the in-class activity and scientific paper presentations done in pairs. In the third block of the course, students in groups of 2 will present a scientific paper of their choice that uses computational methods. Their task will be to present the main findings in up to 30 minutes presentation and answer questions asked by two prepared disputants, other students, and the instructor.
Students are allowed to miss up to 2 classes without any formal excuse (i.e. sick leave). An additional 2 classes can be missed in case of a formal excuse. However, students are encouraged to schedule a meeting with the instructor during office hours if they miss a class.

Literatura

Bail, C. A. (2014). The cultural environment: Measuring culture with big data. Theory and Society, 43(3), 465–482. https://doi.org/10.1007/s11186-014-9216-5
Biesaga, M., Domaradzka, A., Roszczyńska-Kurasińska, M., Talaga, S., & Nowak, A. (2023). The effect of the pandemic on European narratives on smart cities and surveillance. Urban Studies, 004209802211383. https://doi.org/10.1177/00420980221138317
Carley, K. (1994). Extracting culture through textual analysis. Poetics, 22(4), 291–312. https://doi.org/10.1016/0304-422X(94)90011-6
Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380.
Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110
Lazer, D., Pentland, A. (Sandy), 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). Life in the network: The coming age of computational social science. Science (New York, N.Y.), 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714–12719. https://doi.org/10.1073/pnas.1710966114
Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 61–67.
Nowak, A., Rychwalska, A., & Borkowski, W. (2013). Why Simulate? To Develop a Mental Model. Journal of Artificial Societies and Social Simulation, 16(3), 12. https://doi.org/10.18564/jasss.2235
Tjaden, J. (2021). Measuring migration 2.0: A review of digital data sources. Comparative Migration Studies, 9(1), 59. https://doi.org/10.1186/s40878-021-00273-x

Uwagi

W cyklu 2024L:

Students must respect the principles of academic integrity. Cheating and plagiarism (including copying work from other students, LLM, the internet, or other sources) are serious violations that are punishable and instructors are required to report all cases to the administration.
This is the first edition of this class in its current form. Therefore while the exact content and bibliography may change the overall structure of the class and covered topics will stay intact.

Więcej informacji

Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb:

Strona przedmiotu 3700-MSNS-24-ICSS w USOSweb